CN109583946A - A kind of forecasting system and method for active users - Google Patents

A kind of forecasting system and method for active users Download PDF

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CN109583946A
CN109583946A CN201811368717.6A CN201811368717A CN109583946A CN 109583946 A CN109583946 A CN 109583946A CN 201811368717 A CN201811368717 A CN 201811368717A CN 109583946 A CN109583946 A CN 109583946A
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day
predicted
user
days
users
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马朝博
冯晓明
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Beijing Qihoo Technology Co Ltd
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Beijing Qihoo Technology Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities

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Abstract

The present invention discloses the forecasting system and method for a kind of active users, and system includes: application layer, and the input for obtaining user operates, and is operated based on input and determine target date；Computation layer obtains prediction result for the active users using the first prediction module and/or the second prediction module prediction target product in target date；First prediction module and the second prediction module are arranged in computation layer.The input operation of user is obtained by application layer, and it is operated based on input and determines target date, computation layer judges whether target date is the specific date using the first prediction module, and the active users based on judging result prediction target date, and/or, using the second prediction module obtain data Layer on target product upper a cycle historical user's number, and the active users based on historical user's number prediction target date, and prediction result is exported by application layer, to improve the precision of prediction result, to support the adjustment and cost budgeting of Promotion Strategy.

Description

A kind of forecasting system and method for active users
Technical field
The present invention relates to computer science and technology field more particularly to the forecasting systems and method of a kind of active users.
Background technique
DAU (Daily Active User, day any active ues quantity) is usually used in reflecting website, Internet application or network The traffic-operating period of game.DAU was usually counted within (statistics day) on the one, logged in or used the number of users (removal of some product The user of repeat logon), this is similar to visitor's (UV) concept in traffic statistics tool.As reflection website, Internet application Or the important indicator of the traffic-operating period of online game, DAU can provide some data for channel promotion, Cost evaluating and support and help It helps.
It in the prior art, is the prediction of the empirical value progress active users based on user, but the mistake of its prediction result Rate is higher, leads to the adjustment for being not enough to support Promotion Strategy and cost budgeting.
Summary of the invention
The application provides the forecasting system and method for a kind of active users, solves the mistake of prediction result in the prior art The technical issues of rate is higher, is not enough to support the adjustment and cost budgeting of Promotion Strategy.
The application provides a kind of forecasting system of active users characterized by comprising
Application layer, the input for obtaining user operates, and is operated based on the input and determine target date；
Computation layer, for predicting the target product in the mesh using the first prediction module and/or the second prediction module The active users on date are marked, prediction result is obtained；Wherein, first prediction module and second prediction module setting exist In the computation layer；First prediction module is tied for judging whether the target date is the specific date, and based on judgement Fruit predicts the active users of the target date；Second prediction module is for obtaining the target product in a upper week Historical user's number of phase, and predict based on historical user's number the active users of the target date；
Data Layer, for storing historical user's number and the prediction result；
The application layer is also used to obtain the prediction result from data Layer, and exports the prediction result.
Preferably, first prediction module, is specifically used for:
Obtain the target date；Judge whether the target date is the specific date；If so, according to the particular day The estimation parameter and the target date corresponding time sequential value and its estimation parameter of phase, predicts enlivening for the target date Number of users.
Preferably, the specific date includes one of following date or a variety of:
Weekend, the Spring Festival, National Day, other legal festivals and holidays.
Preferably, first prediction module, is specifically used for:
Based on DAU=a1*t+a2*xw+a3*xc+a4*xg+a5*xo+ C predicts the active users of the target date；Its In, DAU is the active users of the target date, and t is the time sequential value, a1Join for the estimation of the time sequential value Number；a2For the estimation parameter at weekend, when the target date is weekend, xW=1, when the target date is not weekend, xW =0；a3For the estimation parameter in the Spring Festival, when the target date is the Spring Festival, xc=1, when the target date is not the Spring Festival, xc=0；a4For the estimation parameter on National Day, when the target date is National Day, xg=1, it is not state in the target date When celebrating section, xg=0；a5For the estimation parameter in other legal red-letter days, when the target date is other legal red-letter days, xo=1, When the target date is not other legal red-letter days, xo=0；C is constant term.
Preferably, first prediction module, is also used to:
Obtain sample historical data；According to the sample historical data, estimation parameter and the institute of the specific date are determined State time sequential value estimation parameter.
Preferably, first prediction module, is also used to:
Determine the earliest date of the data in the sample historical data；The target date is calculated from the earliest date The arrangement value risen, and using the arrangement value as the time sequential value.
Preferably, second prediction module, is specifically used for:
Obtain target date from the application layer, the target date be it is to be predicted on the day of when, from the number The target product is obtained in historical user's number of upper a cycle according to layer；The day of estimating for obtaining the same day to be predicted Adds User Number；Based on historical user's number, old user's number on the same day to be predicted is determined；Day was estimated according to the same day to be predicted Add User several and the same day to be predicted old user's number, obtains same day active users to be predicted.
Preferably, second prediction module, is specifically used for:
When the target date is to be predicted the 1st day, obtain to be predicted the 1st day estimates the number that Adds User day； The several and described historical user's number that Adds User day was estimated according to the same day to be predicted, determines the new of to be predicted the 1st day Increase and retains number of users；According to old user's number on the same day to be predicted, determine that the 1st day old user to be predicted retains number； According to historical user's number, the 1st day reflux number of users to be predicted is determined；According to described 1st day to be predicted estimate Add User day number, the 1st day newly-increased retention number of users to be predicted, the 1st day old user retention to be predicted Several and the 1st day reflux number of users to be predicted, obtains the 1st day active users to be predicted.
Preferably, second prediction module, is specifically used for:
According to historical user's number, determine historical user's number in the 1st day reflux to product ratio to be predicted；Obtain institute State historical user's number and its in the product of the 1st day reflux to product ratio to be predicted, used as the reflux in the 1st day to be predicted Amount.
Preferably, second prediction module, is specifically used for:
When the target date is to be predicted the N days, it is based on historical user's number, determines to be predicted the N days Newly-increased retention number of users, the N days old users to be predicted retain number, the N days reflux numbers of users to be predicted and to Number of users is retained in reflux in the N days of prediction；Obtain to be predicted the N days estimates the number that Adds User day；According to described to be predicted Estimate the number that Adds User day, the N days newly-increased retention numbers of users to be predicted, the N days to be predicted described within the N days Old user retain number and described the N days to be predicted reflux numbers of users, the retention use of reflux in the N days to be predicted Amount predicts the N days day active users to be predicted, N >=2.
Preferably, second prediction module, is specifically used for:
Based on historical user's number, determines that old user's number on the same day to be predicted is old to be predicted the N days and use Family retention ratio；According to old user's number on the same day to be predicted and old user's number on the same day to be predicted in N to be predicted Its old user's retention ratio determines that the N days old users to be predicted retain number.
Preferably, second prediction module, is specifically used for:
Obtaining same day to be predicted to the N-1 days estimated the number that Adds User day；According to historical user's number, determine to Daily estimating Added User number day in the N days newly-increased retention ratios to be predicted in the same day to the N-1 days of prediction；According to institute The same day to be predicted to the N-1 days estimate is stated to Add User day number and its respectively in the N days newly-increased retention ratios to be predicted, Obtain the N days newly-increased retention numbers of users to be predicted.
Preferably, second prediction module, is specifically used for:
According to historical user's number, determine historical user's number in the N days reflux to product ratios to be predicted；According to institute It states historical user's number and its in the N days reflux to product ratios to be predicted, determines the N days reflux numbers of users to be predicted.
Preferably, second prediction module, is specifically used for:
To be predicted the 1st day is obtained to the N-1 days reflux users to be predicted；According to historical user's number, determine to Daily reflux user is in the N days reflux retention ratios to be predicted in the 1st day to the N-1 days of prediction；According to described to pre- Survey the 1st day to the N-1 days reflux users and its respectively in the N days reflux retention ratios to be predicted, determine described to be predicted Reflux in the N days retain number of users.
Preferably, second prediction module, is specifically used for:
According to historical user's number, determine historical user's number in i-th day reflux to product ratio to be predicted；Obtain institute State historical user's number and its in the product of i-th day reflux to product ratio to be predicted, used as the reflux in i-th day to be predicted Family, 1≤i≤N-1.
The application also provides a kind of prediction technique of active users, comprising:
The input operation of user is obtained, and is operated based on the input and determines target date；
The target product enlivening in the target date is predicted using the first prediction module and/or the second prediction module Number of users obtains prediction result；
Wherein, first prediction module is tied for judging whether the target date is the specific date, and based on judgement Fruit predicts the active users of the target date；Second prediction module is for obtaining the target product in a upper week Historical user's number of phase, and predict based on historical user's number the active users of the target date.
Preferably, described to predict the target product in any active ues of the target date using the first prediction module Number obtains prediction result, comprising:
Obtain the target date；
Judge whether the target date is the specific date；
If so, according to the estimation parameter of the specific date and the target date corresponding time sequential value and its estimating Parameter is counted, predicts the active users of the target date.
Preferably, the specific date includes one of following date or a variety of: weekend, the Spring Festival, National Day, Qi Tafa Determine festivals or holidays.
Preferably, the estimation parameter and the target date corresponding time sequential value according to the specific date and It estimates parameter, predicts the active users of the target date, comprising:
Based on DAU=a1*t+a2*xw+a3*xc+a4*xg+a5*xo+ C predicts the active users of the target date；
Wherein, DAU is the active users of the target date, and t is the time sequential value, a1For the time series The estimation parameter of value；a2For the estimation parameter at weekend, when the target date is weekend, xW=1, the target date not When for weekend, xW=0；a3For the estimation parameter in the Spring Festival, when the target date is the Spring Festival, xc=1, in the target date When not being the Spring Festival, xc=0；a4For the estimation parameter on National Day, when the target date is National Day, xg=1, in the mesh When the mark date is not National Day, xg=0；a5It is other legal sections in the target date for the estimation parameter in other legal red-letter days When day, xo=1, when the target date is not other legal red-letter days, xo=0；C is constant term.
Preferably, the method also includes: obtain sample historical data；According to the sample historical data, determine described in The estimation parameter of specific date and the time sequential value estimate parameter.
Preferably, the method also includes: determine the earliest date of the data in the sample historical data；Described in calculating Arrangement value of the target date from the earliest date, and using the arrangement value as the time sequential value.
Preferably, second prediction module predicts that the target product in the active users of the target date, obtains Obtain prediction result, comprising:
Obtain the target date, the target date be it is to be predicted on the day of when, obtain the target product upper Historical user's number of a cycle；
Obtain the same day to be predicted estimates the number that Adds User day；
Based on historical user's number, old user's number on the same day to be predicted is determined；
According to the old user's number for estimating Add User day several and the same day to be predicted on the same day to be predicted, obtain Obtain same day active users to be predicted.
Preferably, second prediction module predicts that the target product in the active users of the target date, obtains Obtain prediction result, comprising:
When the target date is to be predicted the 1st day, obtain to be predicted the 1st day estimates the number that Adds User day；
The several and described historical user's number that Adds User day was estimated according to the same day to be predicted, determines to be predicted the 1 day newly-increased retention number of users；
According to old user's number on the same day to be predicted, determine that the 1st day old user to be predicted retains number；
According to historical user's number, the 1st day reflux number of users to be predicted is determined；
It is used according to several, the described newly-increased retention in 1st day to be predicted that Adds User day of estimating in the 1st day to be predicted Amount, the 1st day old user to be predicted retain number and the 1st day reflux number of users to be predicted, obtain to pre- The 1st day active users surveyed.
Preferably, described according to historical user's number, determine the 1st day reflux number of users to be predicted, comprising:
According to historical user's number, determine historical user's number in the 1st day reflux to product ratio to be predicted；
Obtain historical user's number and its 1st day reflux to product ratio to be predicted product, as described to be predicted The 1st day reflux number of users.
25, method as claimed in claim 23, which is characterized in that it is described according to historical user's number, it determines to pre- The 1st day reflux number of users surveyed, comprising:
When the target date is to be predicted the N days, it is based on historical user's number, determines to be predicted the N days Newly-increased retention number of users, the N days old users to be predicted retain number, the N days reflux numbers of users to be predicted and to Number of users is retained in reflux in the N days of prediction；
Obtain to be predicted the N days estimates the number that Adds User day；
It is used according to several, the described newly-increased retention in the N days to be predicted that Adds User day of estimating in the N days to be predicted Amount, the N days old users to be predicted retain number and the N days reflux numbers of users to be predicted, it is described to Number of users is retained in reflux in the N days of prediction, predicts the N days day active users to be predicted, N >=2.
Preferably, described to be based on historical user's number, determine that the N days old users to be predicted retain number, comprising:
Based on historical user's number, determines that old user's number on the same day to be predicted is old to be predicted the N days and use Family retention ratio；
According to old user's number on the same day to be predicted and old user's number on the same day to be predicted to be predicted the N days Old user's retention ratio determines that the N days old users to be predicted retain number.
Preferably, described to be based on historical user's number, determine the newly-increased retention number of users of to be predicted the N days, comprising:
Obtaining same day to be predicted to the N-1 days estimated the number that Adds User day；
According to historical user's number, determined in the same day to the N-1 days to be predicted and daily to estimate the number that Adds User day In the N days newly-increased retention ratios to be predicted；
It was Added User day number and its respectively in N to be predicted according to the same day to be predicted to the N-1 days estimate It newly-increased retention ratio obtains the N days newly-increased retention numbers of users to be predicted.
Preferably, described to be based on historical user's number, determine the N days reflux numbers of users to be predicted, comprising:
According to historical user's number, determine historical user's number in the N days reflux to product ratios to be predicted；
According to historical user's number and its in the N days reflux to product ratios to be predicted, determine described the N days to be predicted Reflux number of users.
Preferably, described to be based on historical user's number, determine that number of users is retained in reflux in the N days to be predicted, comprising:
To be predicted the 1st day is obtained to the N-1 days reflux users to be predicted；
According to historical user's number, determine reflux user daily in be predicted the 1st day to the N-1 days to be predicted The N days reflux retention ratios；
It is stayed according to the 1st day to the N-1 days reflux users to be predicted and its respectively in reflux in the N days to be predicted Rate is deposited, determines that number of users is retained in the reflux in the N days to be predicted.
Preferably, the method also includes:
According to historical user's number, determine historical user's number in i-th day reflux to product ratio to be predicted；Obtain institute State historical user's number and its in the product of i-th day reflux to product ratio to be predicted, used as the reflux in i-th day to be predicted Family, 1≤i≤N-1.
The application also provides a kind of computer readable storage medium, is stored thereon with computer program, and the program is processed The method and step is realized when device executes.
The application has the beneficial effect that:
The application is operated by the input that application layer obtains user, and is operated based on the input and determined target date, meter It calculates layer and judges whether the target date is the specific date using the first prediction module, and the target is predicted based on judging result The active users on date, and/or, using the target product on the second prediction module acquisition data Layer in upper a cycle Historical user's number, and predict based on historical user's number the active users of the target date, and pass through application layer and export Prediction result, to support the adjustment and cost budgeting of Promotion Strategy, solves the prior art to improve the precision of prediction result The technical issues of error rate of middle prediction result is higher, is not enough to support the adjustment and cost budgeting of Promotion Strategy.
The above description is only an overview of the technical scheme of the present invention, in order to better understand the technical means of the present invention, And it can be implemented in accordance with the contents of the specification, and in order to allow above and other objects of the present invention, feature and advantage can It is clearer and more comprehensible, the followings are specific embodiments of the present invention.
Detailed description of the invention
By reading the following detailed description of the preferred embodiment, various other advantages and benefits are common for this field Technical staff will become clear.The drawings are only for the purpose of illustrating a preferred embodiment, and is not considered as to the present invention Limitation.And throughout the drawings, the same reference numbers will be used to refer to the same parts.In the accompanying drawings:
Fig. 1 is a kind of structural schematic diagram of the forecasting system of active users provided by the present application；
Fig. 2 is the composition figure of the active users of the second prediction module prediction of the forecasting system in Fig. 1；
Fig. 3 is a kind of flow chart of the prediction technique of active users provided by the present application；
Fig. 4 is a kind of structural schematic diagram of computer readable storage medium provided by the present application.
Specific embodiment
Exemplary embodiments of the present disclosure are described in more detail below with reference to accompanying drawings.Although showing the disclosure in attached drawing Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here It is limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure It is fully disclosed to those skilled in the art.
Embodiment one
The present embodiment provides a kind of forecasting system of active users, active users are DAU (Daily Active User, day any active ues quantity), be usually used in reflecting the traffic-operating period of website, Internet application or online game.DAU usually unites Within meter one day (statistics day), the number of users (user of removal repeat logon) of some product is logged in or used.This method can For predicting the work of the products such as website, Internet application (such as mobile phone assistant, mobile phone bodyguard, fast video, live streaming), online game Jump number of users.
As shown in Figure 1, the forecasting system of the active users, comprising: application layer, computation layer and data Layer, application layer, It is connected with each other between computation layer and data Layer.
After user carries out input operation, the application layer is used to obtain the input operation of user, and is based on the input It operates and determines target date.Specifically, the application layer includes input module and output module.Input module is for receiving user Input operation, the output module is for exporting prediction result.
The computation layer, for predicting the target product in institute using the first prediction module and/or the second prediction module The active users of target date are stated, prediction result is obtained.Wherein, first prediction module and second prediction module are set It sets in the computation layer.First prediction module is based on sentencing for judging whether the target date is the specific date The active users of target date described in disconnected prediction of result.Second prediction module is for obtaining the target product upper one Historical user's number in a period, and predict based on historical user's number the active users of the target date.That is, both can be with The active users of target date are calculated by the first prediction module, target date can also be calculated by the second prediction module Active users, or any active ues of the target date are calculated by the first prediction module and the second prediction module simultaneously Number.
The data Layer, for storing historical user's number and the prediction result.The prediction result can also be with It is stored in the data Layer, specifically, the data Layer includes multiple databases such as database 1, database 2, database 3, no Same data can be placed in different databases, for example historical user's number can be placed in database 1, and prediction result can be put In database 2.
The application layer is also used to obtain the prediction result from data Layer, and exports the prediction result.
It is additionally provided with historical query module on the application layer, when getting the inquiry operation of user, is looked into data Layer Historical data is ask, for example, any day active users in last period.
It is additionally provided with statistical module on the application layer, it is active that such as one day any a period of time, one week, one month can be counted Number of users.
The first, the first prediction module is described in detail:
Specifically, first prediction module, is specifically used for:
Obtain the target date；Judge whether the target date is the specific date；If so, according to the particular day The estimation parameter and the target date corresponding time sequential value and its estimation parameter of phase, predicts enlivening for the target date Number of users.
In the specific implementation process, it first to obtain and need to predict which day the following active users, such as it needs to be determined that not Come the 30th day, the 50th day, the 120th day, the 180th day active users, rear extended meeting is to determine the 30th day, the 50th day, the 120th It, the 180th day active users are illustrated.
As a kind of optional embodiment, the specific date includes one of following date or a variety of: weekend, the Spring Festival, state Celebrate section, other legal festivals and holidays, new projects' popularization day etc..Assuming that following 30th day be weekend, the 50th day future was National Day, not Coming the 120th day for other legal days and weekend, the 180th day is the Spring Festival.
In the specific implementation process, a specific date corresponding estimation parameter, the corresponding estimation parameter of time sequential value, According to specific date, the estimation parameter of specific date, the estimation parameter of time sequential value and time sequential value, can predict The active users of target date.Wherein, estimation parameter can be arranged based on experience value, can also be obtained by historical data.
Specifically, first prediction module, is also used to: obtaining sample historical data；According to the sample historical data, Determine the estimation parameter and time sequential value estimation parameter of the specific date.
In the specific implementation process, sample historical data is the data extracted as sample from historical data, usually Sample historical data can take the data in the past period, and such as data according to the past 1 year carry out the meter of estimation parameter It calculates, then 1 year data is sample historical data in the past.Calculate estimate parameter when, specifically can be by by sample historical data It substitutes into calculation formula to be calculated, can be obtained the estimation parameter and time sequential value estimation parameter of specific date, referring specifically to Subsequent descriptions.
In the specific implementation process, time sequential value refers to: the time that the numerical value of same statistical indicator is occurred by it is first Ordered series of numbers value made of sequence arranges afterwards.The main purpose of time series analysis is to be carried out in advance according to existing historical data to future It surveys.The difference of time according to the observation, the time in time series can be time, season, month or other any time forms. In the present embodiment, time series is indicated according to the time form in day.
Specifically, first prediction module, is also used to: determine the data in the sample historical data most early Phase；Arrangement value of the target date from the earliest date is calculated, and using the arrangement value as the time sequential value.
For example, if the data that sample historical data is 1 year in the past, the earliest date is 1 year in historical data Before, since the year before, the time sequential value of target date is calculated, if target date is following the 30th day, time series Value is equal to 365+30, similarly, if target date is following the 50th day, time sequential value 365+50.
Specifically, in the estimation parameter and time sequential value and its estimation parameter according to the specific date, target date is predicted Active users when, can be in the following way:
Specifically, first prediction is specifically used for: based on lower formula (4), predict the active users of target date:
DAU=a1*t+a2*xw+a3*xc+a4*xg+a5*xo+ C ... ... formula (4)
Wherein, DAU is the active users of target date, and t is time sequential value, a1Join for the estimation of time sequential value Number；a2For the estimation parameter at weekend, when target date is weekend, xW=1, when target date is not weekend, xW=0；a3For The estimation parameter in the Spring Festival, when target date is the Spring Festival, xc=1, when target date is not the Spring Festival, xc=0；a4For National Day Estimation parameter, target date be National Day when, xg=1, when target date is not National Day, xg=0；a5For other methods The estimation parameter for determining red-letter day, when target date is other legal red-letter days, xo=1, it is not other legal red-letter days in target date When, xo=0；C is constant term.
In the specific implementation process, a1、a2、a3、a4And a5, can be empirical value, can also be to be obtained by historical data. When obtaining by historical data, obtain sample historical data, include in historical data the specific date active users and its Corresponding time sequential value, by bringing active users and its corresponding time sequential value into above-mentioned calculation formula (2) Obtain a1、a2、a3、a4And a5。
It should be noted that when bringing historical data into above-mentioned formula (2) calculating, when the specific date is the Spring Festival, xc Equal to 1, when the specific date is not the Spring Festival, xcEqual to 0；When the specific date is weekend, xWIt is not week in the specific date equal to 1 When last, xWEqual to 0；When the specific date is National Day, xgEqual to 1, when the specific date is not National Day, xgEqual to 0；In spy When fixing the date as other legal red-letter days, xoEqual to 1, when the specific date is not other legal red-letter days, xoEqual to 0；
For example, in a1、a2、a3、a4And a5And after C is determined, the 30th day, the 50th day, the 120th day, the can be calculated 180 days active users.
Firstly, determining the 30th day, the 50th day, the 120th day, the 180th day time sequential value, the 30th day time series respectively Value is that 395, the 50th days time sequential values are 415, and it is 545 that the 120th day time sequential value, which is the 485, the 180th day time sequential value,.
30th day time sequential value is substituted into DAU=a1*t+a2*xw+a3*xc+a4*xg+a5*xoIn+C formula, the 30th day It is not legal red-letter day, therefore, x for weekendwIt is 1, xc、xgAnd xoIt is equal to the 0, the 30th day DAU=a1*t+a2+ C, due to a1、 a2, t and C be it is known that the 30th day active users can be calculated.
50th day time sequential value is substituted into DAU=a1*t+a2*xw+a3*xc+a4*xg+a5*xoIn+C formula, the 50th day It is not weekend for National Day, therefore, xgIt is 1, xc、xwAnd xoIt is equal to the 0, the 50th day DAU=a1*t+a4+ C, due to a1、a4、t And C is it is known that the 50th day active users can be calculated.
120th day time sequential value is substituted into DAU=a1*t+a2*xw+a3*xc+a4*xg+a5*xoIn+C formula, the 120th It is other legal days and weekend, therefore, xo、xwIt is 1, xc、xgIt is equal to the 0, the 120th day DAU=a1*t+a2+a5+ C, due to a1、a2、a5, t and C be it is known that the 120th day active users can be calculated.
180th day time sequential value is substituted into DAU=a1*t+a2*xw+a3*xc+a4*xg+a5*xoIn+C formula, the 180th It is the Spring Festival, is not weekend, therefore, xcIt is 1, xw、xg、xoIt is equal to the 0, the 180th day DAU=a1*t+a3+ C, due to a1、a3、t And C is it is known that the 180th day active users can be calculated.
Specifically, a1Can be set to 60~75 (such as: 60 or 65 or 67 or 70 or 75, etc.), a2It can be set For 180~230 (such as: 180 or 190 or 209 or 215 or 230 etc.), a3Can be set to -25~-35 (such as: - 25 or -27 or -30 or -33 or -35 etc.), a5Can be set to 4~10 (such as: 4 or 5 or 6 or 7 or 10 etc. Deng), C can be set to 3~6 (such as: 3 or 3.8 or 4.09 or 4.86 or 6 etc.).
Specifically, a1、a2、a3、a4And a5And the value of C can be adjusted as the case may be, and e.g., DAU=60*t+ 180*xw+-25*xc+4*xo+ 3, DAU=67*t+209*xw+-30*xc+7*xo+ 4.09, DAU=75*t+230*xw+-35*xc+ 10*xo+ 6, certainly, specific value is not limited to above situation.
If target date is not the specific date, directly according to the target date corresponding time sequential value and its estimation Parameter predicts the active users of target date, that is, DAU=a1* t+C, wherein DAU is the active users of target date, t For time sequential value, a1 is the estimation parameter of time sequential value；C is constant term.
The second, the second prediction module is described in detail:
Before introducing second prediction module, the noun used first to needs is explained.
Historical user refers to the user for using the product in a period.
Flow back user, in the historical user for referring to a period, still in the user using the product within this period.Example Such as, the 1st day reflux user to be predicted refers to using the production to be predicted the 1st day in the historical user in a period The user of product；The N days reflux users to be predicted, refer in the historical user in a period on day 1 to the N-1 days not Using the product, but in the N days users using the product.
Reflux to product ratio refers to that historical user becomes the probability of reflux user.
User is retained in reflux, refers to reflux user in the subsequent user for still using the product in this period.For example, N User is retained in it reflux, refers in the 1st day to the N-1 days reflux users at the N days still in the user using the product.
Flow back retention ratio, refers to reflux user in the subsequent probability using the product in this period.For example, to be predicted The q days reflux users in the N days reflux retention ratios to be predicted, refer to the q days reflux users to be predicted to The N days of prediction still use the probability of the product.
The old user on the same day to be predicted refers to still making on the day of this period in the historical user in a upper period With the user of the product." same day " herein can be understood as " today ".
Old user retains, and refers to that the old user on the same day to be predicted is subsequent also in the use using the product in this period Family.For example, the N days old users retain, refer to that old user's number on the same day to be predicted was still using the product at the N days User.
The N days old user's retention ratios to be predicted refer to that the old user on the same day still used to be predicted the N days and are somebody's turn to do The probability of product.
Day Adds User, and refers to the user that the product was not used in the past but had used the product on the same day.
It estimates and Adds User day, refer to the user that the product was not used in the past but had used the product on the same day, this It is not practical situation, only estimates.It is newly-increased to retain user and refer to, it Adds User day and subsequent still uses the product in this period User.
Estimating for jth day to be predicted Adds User in the N days newly-increased retention ratios to be predicted day, refers to pre- Estimating for the jth day of survey Adds User to be predicted the N days still using the probability of the product day.
1, the active users that the same day to be predicted how is obtained by second prediction module introduced.
Specifically, second prediction module, is specifically used for: the target date is obtained from the application layer, described When target date is the same day to be predicted, the target product is obtained in the historical user of upper a cycle from the data Layer Number；Obtain the same day to be predicted estimates the number that Adds User day；Based on historical user's number, the old of the same day to be predicted is determined Number of users；According to the old user's number for estimating Add User day several and the same day to be predicted on the same day to be predicted, obtain Obtain same day active users to be predicted.
The length in (statistics) period, which can according to need, to be configured, such as is set as 20 days, 30 days, 50 days.In this reality It applies in mode, preferably 31 days, a subsequent period is illustrated for 31, but is not limited to 31 days.The history in a upper period is used Amount, refers to the number of users crossed in upper a cycle using the product, old user's number in an either upper period, one week upper The number that Adds User, the reflux number of users in a upper period of phase, is included in interior.If the period is 31 days, the history in a upper period Number of users refers to number of users in 31 days before the same day.
Wherein, the day of estimating on the same day Adds User, and refers to that the product was not used before estimating but the same day has used this The user of product, be not it is actual, only estimate.The old user on the same day to be predicted referred to the history in a upper period Still in the user using the product on the day of this period in user.
Specifically, the old user's number for estimating several and to be predicted same day that Adds User day on the same day to be predicted is summed, It can be obtained the active users on the same day to be predicted.That is, as shown in Fig. 2, the active users on the same day be equal to old user's number with Add User several sums.
Specifically, old user's number on the same day to be predicted can be obtained by following manner: be obtained according to historical user's number The average value of the daily active users in a upper period was subtracted a upper period by the average value of the daily active users in one period Several average values that Add User daily, obtain old user's number on the same day to be predicted.
2, introduce the 1st day active users to be predicted how are obtained by second prediction module
According to historical user's number and the active users on the same day to be predicted in a upper period, to be predicted the 1st day was predicted Active users.To be predicted the 1st day refers to the 1st day after the same day, it is subsequent in be predicted the 2nd day mentioned be the same day Afterwards the 2nd day, to be predicted the N days were the N days after the same day.
Specifically, second prediction module, is specifically used for: when the target date is to be predicted the 1st day, obtaining Estimate the number that Adds User day in be predicted the 1st day；According to the same day to be predicted estimate Add User day it is several and described Historical user's number determines the 1st day newly-increased retention number of users to be predicted；According to old user's number on the same day to be predicted, Determine that the 1st day old user to be predicted retains number；According to historical user's number, determine that reflux in the 1st day to be predicted is used Amount；The number that Adds User day, the 1st day newly-increased retention users to be predicted were estimated according to described 1st day to be predicted Several, the described 1st day old user to be predicted retains number and the 1st day reflux number of users to be predicted, obtains to be predicted The 1st day active users.
Wherein, the 1st day newly-increased retention user to be predicted refers in the estimating and Add User day of the same day to be predicted Still using the product user on day 1.1st day old user to be predicted retains the old use for referring to the same day to be predicted The user of the product is still used in family on day 1.1st day reflux user to be predicted refers to that the history in a period is used At to be predicted the 1st day still in the user using the product in family.
Specifically, as shown in Fig. 2, can will acquire to be predicted the 1st day estimates the number that Adds User day, to be predicted the 1 day newly-increased retention number of users, the 1st day old user to be predicted retain the reflux number of users of number and to be predicted the 1st day With as the 1st day active users to be predicted.
Specifically, the 1st day newly-increased retention number of users to be predicted can obtain in the following way, and described second Prediction module is specifically used for: according to historical user's number, determine the same day to be predicted estimates the number that Adds User day to be predicted 1st day newly-increased retention ratio；Obtain the same day to be predicted estimates the number and it is newly-increased to be predicted the 1st day of Adding User day The product of retention ratio, as the 1st day newly-increased retention number of users to be predicted.
The same day to be predicted estimates the number that Adds User day in the specific calculating side of the 1st day newly-increased retention ratio to be predicted Formula is as follows, and second prediction module is specifically used for: obtaining the 1st day in upper period, the 2nd day, the 7th day, the 30th day new Increase retention ratio C1X、C2X、C7X、C30X；Based on formula (one), obtain the same day to be predicted estimates the number that Adds User day to be predicted The 1st day newly-increased retention ratio.
yj=a2*xb2Formula (one)
Wherein, a2=C1X, b2=average (log2(C2X/C1X), log7(C7X/C1X), log30(C30X/C1X)), x is equal to 1。
Specifically, the 1st day old user to be predicted retains number and can obtain in the following manner, the second prediction mould Block is specifically used for: according to historical user's number, determining that old user's number on the same day to be predicted is stayed in the 1st day old user to be predicted Deposit rate；The old user's number and old user's number on the same day to be predicted for obtaining the same day to be predicted are in the 1st day old use to be predicted The product of family retention ratio retains number as the 1st day old user to be predicted.
Old user's number on the same day to be predicted the 1st day old user's retention ratio to be predicted specific calculation such as Under, second prediction module is specifically used for: obtaining the 1st day in upper period, the 2nd day, the 7th day, the 30th day old user Retention ratio C1L、C2L、C7L、C30L；Based on formula (two), the old user's number for obtaining the same day to be predicted is old to be predicted the 1st day User's retention ratio.
yL=a1*xb1Formula (two)
Wherein, a1=C1L, b1=average (log2(C2L/C1L), log7(C7L/C1L), log30(C30L/C1L)), x is equal to 1。
Specifically, the 1st day reflux number of users to be predicted can obtain in the following manner, the second prediction module tool Body is used for: according to historical user's number, determining historical user's number in the 1st day reflux to product ratio to be predicted, reflux to product ratio is equal to upper Active users of the active users average value in one period except the same day in the above period；It obtains historical user's number and history is used Amount the 1st day reflux to product ratio to be predicted product, as the 1st day reflux number of users to be predicted.
3, introduce the N days active users to be predicted how are obtained by second prediction module, N is more than or equal to 2。
It, can be according to historical user's number, any active ues on the same day after the 1st day active users after on the day of acquisition 1st day active users several, to be predicted, calculate the N days active users to be predicted.
Specifically, second prediction module, is specifically used for: when the target date is to be predicted the N days, being based on Historical user's number, determine the newly-increased retention number of users of to be predicted the N days, the N days old users to be predicted retain number, Number of users is retained in the reflux number of users of to be predicted the N days and reflux in the N days to be predicted；Obtain to be predicted the N days Estimate the number that Adds User day；The number that Adds User day, described the N days to be predicted were estimated according to described the N days to be predicted It is newly-increased to retain number of users, the N days old users retention number to be predicted and the use of reflux in the N days to be predicted Amount, the retention number of users of reflux in the N days to be predicted, prediction the N days day active users to be predicted, N >= 2。
Wherein, to be predicted to Add User the N days days of estimating, refer to being not used before prediction the product but to The N days of prediction use the user of the product.
Specifically, several, to be predicted the N days that Add User day are estimated as shown in Fig. 2, obtaining the N days to be predicted It is newly-increased to retain number of users, the N days old users retention number and the N days reflux numbers of users to be predicted to be predicted, to pre- The sum of number of users is retained in the reflux in the N days surveyed, as the N days day active users to be predicted.
Such as: the 2nd day active users to be predicted equal to be predicted the 2nd day estimating Add User day number, to The estimating of the same day of prediction and to be predicted the 1st day Add User day number the 2nd day newly-increased retention number of users to be predicted, to Old user's number on the same day of prediction exists in the 2nd day old user retention number to be predicted, the 1st day reflux number of users to be predicted The reflux of to be predicted the 2nd day retains number of users and historical user's number in the sum of to be predicted the 2nd day reflux number of users.
3rd day active users to be predicted estimating equal to be predicted the 3rd day, which Adds User day, to be counted, is to be predicted The estimating of the same day, the 1st day and the 2nd day Add User day number in the 3rd day newly-increased retention number of users to be predicted, to be predicted work as It old user's number is the 3rd day old user to be predicted retains number, the reflux number of users of to be predicted the 1st day and the 2nd day exists The reflux of to be predicted the 3rd day retains number of users and historical user's number in the sum of to be predicted the 3rd day reflux number of users.
A, the N days old users to be predicted retain number
Specifically, second prediction module, is specifically used for:
Based on historical user's number, determines that old user's number on the same day to be predicted is old to be predicted the N days and use Family retention ratio determines that the old user on the same day to be predicted still used the probability of the product to be predicted the N days；Further according to institute The old user's number and old user's number on the same day to be predicted for stating the same day to be predicted in the N days old user's retention ratios to be predicted, Determine that the N days old users to be predicted retain number.Specifically, obtain old user's number on the same day to be predicted with it is to be predicted The same day old user's number in the N days old user's retention ratio products to be predicted, retained as the N days old users to be predicted Number.
Old user's number on the same day to be predicted can be obtained in the N days old user's retention ratios to be predicted by following manner, Second prediction module, is specifically used for: obtain the 1st day in upper period, the 2nd day, the 7th day, the 30th day old user stay Deposit rate C1L、C2L、C7L、C30L；Based on formula (two), the old user's number for obtaining the same day to be predicted was used always to be predicted the N days Family retention ratio.
yL=a1*xb1(formula two)
Wherein, a1=C1L, b1=average (log2(C2L/C1L), log7(C7L/C1L), log30(C30L/C1L)), x is equal to N。
B, to be predicted the N days newly-increased retention numbers of users
Specifically, second prediction module, is specifically used for: obtain the same day to be predicted to the N-1 days to estimate day new Add amount；According to historical user's number, determined in the same day to the N-1 days to be predicted and daily to estimate the number that Adds User day In the N days newly-increased retention ratios to be predicted；According to the same day to be predicted to the N-1 days estimate Add User day number and It in the N days newly-increased retention ratios to be predicted, obtains the N days newly-increased retention numbers of users to be predicted respectively.
In the present embodiment, the number that Adds User day of estimating in the same day to the N days daily is set as equal, in other realities Apply in mode, in the same day to the N days daily estimating Add User day number may be set to be it is unequal.
Specifically, the specific calculation of to be predicted the N days newly-increased retention numbers of users is as follows, the second prediction mould Block is specifically used for: obtain the same day to be predicted to the N-1 days estimate Add User day it is several with the same day to be predicted to N-1 It estimates the number that Adds User day respectively in the product of the N days newly-increased retention ratios to be predicted, obtains N number of product altogether；It will be N number of Product addition obtains the newly-increased retention number of users of to be predicted the N days.
That is, obtain the 1st day estimate Add User day number and the 1st day estimate Add User day number at the N days using being somebody's turn to do The product of the probability of product obtains the 2nd day number that Adds User day of estimating and Adds User number day at the N days with the 2nd day estimate Using product ... ... the of the probability of the product, obtain the N-1 days estimate Add User day number and the N-1 days estimate day Add User product of the number in the N days probabilities using the product, total N number of product；By N number of product addition, obtain to be predicted The N days newly-increased retention numbers of users
Specifically, daily estimating Adds User number day to be predicted the N days in the same day to the N-1 days to be predicted Newly-increased retention ratio calculation is as follows, and second prediction module is specifically used for: obtain the 1st day in a upper period, the 2nd day, 7th day, the 30th day newly-increased retention ratio C1X、C2X、C7X、C30X；Based on formula (one), obtain jth day to be predicted estimates day Number Add User in the N days newly-increased retention ratios to be predicted.
yj=a2*xb2Formula (one)
Wherein, a2=C1X, b2=average (log2(C2X/C1X), log7(C7X/C1X), log30(C30X/C1X)), x is equal to N-j, 0≤j≤N-1.
C, the N days reflux numbers of users to be predicted
Specifically, second prediction module, is specifically used for: according to historical user's number, determining the historical user Number is in the N days reflux to product ratios to be predicted；According to historical user's number and its in the N days reflux to product ratios to be predicted, Determine the N days reflux numbers of users to be predicted.
The active users average value that reflux to product ratio was equal to a upper period removes the active users on the same day in the above period. For example, by total active users in a upper period divided by 31, obtaining the average value of any active ues when the period is 31 days.
Specifically, in the present embodiment, historical user's number and historical user's number are obtained in reflux in the N days to be predicted The product of coefficient, as the N days reflux users to be predicted.
D, number of users is retained in reflux in the N days to be predicted
Specifically, second prediction module, is specifically used for: obtaining to be predicted the 1st day to be predicted the N-1 days and returns Flow user；According to historical user's number, determine reflux user daily in be predicted the 1st day to the N-1 days to be predicted The N days reflux retention ratios；According to described 1st day to be predicted to the N-1 days reflux users and its respectively to be predicted The N days reflux retention ratios determine that number of users is retained in the reflux in the N days to be predicted.
Specifically, to be predicted the 1st day is obtained to the N-1 days reflux users to be predicted, including following two step:
According to historical user's number, determine historical user's number in i-th day reflux to product ratio to be predicted.When even i=1, really Determine the probability for using the product in historical user on day 1；If when i >=2, it is determined that in historical user, on day 1 to (i-1)-th The product is not used but at i-th day in the probability using the product in it.The active users that reflux to product ratio was equal to a upper period are flat Active users of the mean value except the same day in the above period.For example, when the period is 31 days, by total active users in a upper period Divided by 31, the average value of any active ues is obtained.
Historical user's number and historical user's number are obtained in the product of i-th day reflux to product ratio to be predicted, as to be predicted I-th day reflux user, 1≤i≤N-1.
Specifically, in the present embodiment, the 1st day to the N-1 days reflux users and the to be predicted the 1st to be predicted are obtained It, respectively in the product of the N days reflux retention ratios to be predicted, obtains N-1 product to the N-1 days reflux users altogether；By N- 1 product addition obtains reflux in the N days to be predicted and retains number of users.That is, obtaining the 1st day reflux user and time in the 1st day User is flowed in the product of the N days reflux retention ratios, obtains the 2nd day reflux user and the 2nd day reflux user at the N days The product ... ... for the retention ratio that flows back obtains the N-1 days reflux users and stays with the N-1 days reflux users in reflux in the N days The product of rate is deposited, N-1 product addition is obtained reflux in the N days to be predicted and retain number of users by total N-1 product.
The specific calculation of the N days reflux retention ratios to be predicted is as follows, second prediction module, specific to use In: obtained the 2nd day in upper period, the 3rd day, the 8th day, the 30th day reflux retention ratio C2X、C3X、C8X、C30X；Based on formula (3), the reflux user of to be predicted the q days is obtained in the N days reflux retention ratios to be predicted.
yq=a3*xb3Formula (three)
Wherein, a3=C2X, b3=average (log3(C3H/C2H), log8(C8H/C2H), log30(C30H/C2H)), 1≤q≤ N-1, x are equal to N-q.
The application is operated by the input that application layer obtains user, and is operated based on the input and determined target date, meter It calculates layer and judges whether the target date is the specific date using the first prediction module, and the target is predicted based on judging result The active users on date, and/or, using the target product on the second prediction module acquisition data Layer in upper a cycle Historical user's number, and predict based on historical user's number the active users of the target date, and pass through application layer and export Prediction result, to support the adjustment and cost budgeting of Promotion Strategy, solves the prior art to improve the precision of prediction result The technical issues of error rate of middle prediction result is higher, is not enough to support the adjustment and cost budgeting of Promotion Strategy.
Embodiment two
Based on same inventive concept, the application also provides a kind of prediction technique of active users, as shown in figure 3, institute The method of stating includes:
Step 310, the input operation of user is obtained, and is operated based on the input and determines target date；
Step 320, predict the target product in the target day using the first prediction module and/or the second prediction module The active users of phase obtain prediction result；
Wherein, first prediction module is tied for judging whether the target date is the specific date, and based on judgement Fruit predicts the active users of the target date；Second prediction module is for obtaining the target product in a upper week Historical user's number of phase, and predict based on historical user's number the active users of the target date.
Specifically, described to predict the target product in any active ues of the target date using the first prediction module Number obtains prediction result, comprising: obtain the target date；Judge whether the target date is the specific date；If so, According to the estimation parameter of the specific date and the target date corresponding time sequential value and its parameter is estimated, described in prediction The active users of target date.
Specifically, the specific date includes one of following date or a variety of: weekend, the Spring Festival, National Day, Qi Tafa Determine festivals or holidays.
Specifically, the estimation parameter and the target date corresponding time sequential value according to the specific date and It estimates parameter, predicts the active users of the target date, comprising: is based on DAU=a1*t+a2*xw+a3*xc+a4*xg+a5* xo+ C predicts the active users of the target date；Wherein, DAU is the active users of the target date, and t is described Time sequential value, a1For the estimation parameter of the time sequential value；a2It is week in the target date for the estimation parameter at weekend When last, xW=1, when the target date is not weekend, xW=0；a3For the estimation parameter in the Spring Festival, it is in the target date When the Spring Festival, xc=1, when the target date is not the Spring Festival, xc=0；a4For the estimation parameter on National Day, in the target day When phase is National Day, xg=1, when the target date is not National Day, xg=0；a5Join for the estimation in other legal red-letter days Number, when the target date is other legal red-letter days, xo=1, when the target date is not other legal red-letter days, xo= 0；C is constant term.
Specifically, the method also includes: obtain sample historical data；According to the sample historical data, determine described in The estimation parameter of specific date and the time sequential value estimate parameter.
Specifically, the method also includes: determine the earliest date of the data in the sample historical data；Described in calculating Arrangement value of the target date from the earliest date, and using the arrangement value as the time sequential value.
Specifically, second prediction module predicts that the target product in the active users of the target date, obtains Prediction result, comprising: obtain the target date, the target date be it is to be predicted on the day of when, obtain the target Historical user number of the product in upper a cycle；Obtain the same day to be predicted estimates the number that Adds User day；Based on the history Number of users determines old user's number on the same day to be predicted；The several and institute that Adds User day was estimated according to the same day to be predicted The old user's number for stating the same day to be predicted obtains same day active users to be predicted.
Specifically, second prediction module predicts that the target product in the active users of the target date, obtains Obtain prediction result, comprising: when the target date is to be predicted the 1st day, the day of estimating for obtaining to be predicted the 1st day is increased newly Number of users；The several and described historical user's number that Adds User day was estimated according to the same day to be predicted, determines to be predicted the 1st It newly-increased retention number of users；According to old user's number on the same day to be predicted, determine that the 1st day old user to be predicted stays Deposit number；According to historical user's number, the 1st day reflux number of users to be predicted is determined；According to described 1st day to be predicted Estimate the number that Adds User day, the 1st day newly-increased retention number of users to be predicted, the 1st day old user to be predicted Number and the 1st day reflux number of users to be predicted are retained, the 1st day active users to be predicted are obtained.
Specifically, described according to historical user's number, determine the 1st day reflux number of users to be predicted, comprising: according to Historical user's number determines historical user's number in the 1st day reflux to product ratio to be predicted；Obtain historical user's number With its 1st day reflux to product ratio to be predicted product, as the 1st day reflux number of users to be predicted.
Specifically, described according to historical user's number, determine the 1st day reflux number of users to be predicted, comprising: in institute When to state target date be to be predicted the N days, it is based on historical user's number, determines the use of newly-increased retention in the N days to be predicted Amount, the N days old users retention number to be predicted, to be predicted the N days reflux numbers of users and to be predicted the N days Number of users is retained in reflux；Obtain to be predicted the N days estimates the number that Adds User day；It is pre- according to described the N days to be predicted Estimate several, described the N days newly-increased retention numbers of users to be predicted that Add User day, the N days old users to be predicted stay Deposit several and described the N days reflux numbers of users to be predicted, number of users, prediction institute are retained in the reflux in the N days to be predicted State the N days day active users to be predicted, N >=2.
Specifically, described to be based on historical user's number, determine that the N days old users to be predicted retain number, comprising: base In historical user's number, determine old user's number on the same day to be predicted in the N days old user's retention ratios to be predicted；Root It is retained according to old user's number and old user's number on the same day to be predicted on the same day to be predicted in the N days old users to be predicted Rate determines that the N days old users to be predicted retain number.
Specifically, described to be based on historical user's number, determine the newly-increased retention number of users of to be predicted the N days, comprising: Obtaining same day to be predicted to the N-1 days estimated the number that Adds User day；According to historical user's number, to be predicted work as is determined It Adds User to daily estimating in the N-1 days and counts in the N days newly-increased retention ratios to be predicted day；According to described to be predicted The same day to the N-1 days estimate Add User day number and its respectively in the N days newly-increased retention ratios to be predicted, described in acquisition The newly-increased retention number of users of to be predicted the N days.
Specifically, described to be based on historical user's number, determine the N days reflux numbers of users to be predicted, comprising: according to Historical user's number determines historical user's number in the N days reflux to product ratios to be predicted；According to historical user's number And its in the N days reflux to product ratios to be predicted, determine the N days reflux numbers of users to be predicted.
Specifically, described to be based on historical user's number, determine that number of users is retained in reflux in the N days to be predicted, comprising: obtain Take to be predicted the 1st day to the N-1 days reflux users to be predicted；According to historical user's number, to be predicted the 1st day is determined To reflux user daily in the N-1 days in the N days reflux retention ratios to be predicted；Extremely according to described 1st day to be predicted The N-1 days reflux users and its respectively in the N days reflux retention ratios to be predicted, determine described to be predicted flow back for the N days Retain number of users.
Specifically, the method also includes: according to historical user's number, determine historical user's number to be predicted I-th day reflux to product ratio；Obtain historical user's number and its i-th day reflux to product ratio to be predicted product, as institute State i-th day reflux user to be predicted, 1≤i≤N-1.
The application is operated based on the input by obtaining the input operation of user and determines target date, utilizes first Prediction module judges whether the target date is the specific date, and the active use of the target date is predicted based on judging result Amount, and/or, the target product is obtained in historical user's number of upper a cycle using the second prediction module, and is based on institute It states historical user's number and predicts the active users of the target date, so that the precision of prediction result is improved, to support popularization plan Adjustment and cost budgeting slightly, the error rate for solving prediction result in the prior art is higher, is not enough to support Promotion Strategy The technical issues of adjustment and cost budgeting.
Embodiment three
Based on the same inventive concept, as shown in figure 4, present embodiments providing a kind of computer readable storage medium 400, On be stored with computer program 411, which performs the steps of when being executed by processor
The input operation of user is obtained, and is operated based on the input and determines target date；
The target product enlivening in the target date is predicted using the first prediction module and/or the second prediction module Number of users obtains prediction result；
Wherein, first prediction module is tied for judging whether the target date is the specific date, and based on judgement Fruit predicts the active users of the target date；Second prediction module is for obtaining the target product in a upper week Historical user's number of phase, and predict based on historical user's number the active users of the target date.
In the specific implementation process, it when which is executed by processor, may be implemented any in embodiment one Embodiment.
Algorithm and display are not inherently related to any particular computer, virtual system, or other device provided herein. Various general-purpose systems can also be used together with teachings based herein.As described above, it constructs required by this kind of system Structure be obvious.In addition, the present invention is also not directed to any particular programming language.It should be understood that can use various Programming language realizes summary of the invention described herein, and the description done above to language-specific is to disclose this hair Bright preferred forms.
In the instructions provided here, numerous specific details are set forth.It is to be appreciated, however, that implementation of the invention Example can be practiced without these specific details.In some instances, well known method, structure is not been shown in detail And technology, so as not to obscure the understanding of this specification.
Similarly, it should be understood that in order to simplify the disclosure and help to understand one or more of the various inventive aspects, Above in the description of exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes In example, figure or descriptions thereof.However, the disclosed method should not be interpreted as reflecting the following intention: i.e. required to protect Shield the present invention claims features more more than feature expressly recited in each claim.More precisely, as following Claims reflect as, inventive aspect is all features less than single embodiment disclosed above.Therefore, Thus the claims for following specific embodiment are expressly incorporated in the specific embodiment, wherein each claim itself All as a separate embodiment of the present invention.
Those skilled in the art will understand that can be carried out adaptively to the module in the equipment in embodiment Change and they are arranged in one or more devices different from this embodiment.It can be the module or list in embodiment Member or component are combined into a module or unit or component, and furthermore they can be divided into multiple submodule or subelement or Sub-component.Other than such feature and/or at least some of process or unit exclude each other, it can use any Combination is to all features disclosed in this specification (including adjoint claim, abstract and attached drawing) and so disclosed All process or units of what method or apparatus are combined.Unless expressly stated otherwise, this specification is (including adjoint power Benefit require, abstract and attached drawing) disclosed in each feature can carry out generation with an alternative feature that provides the same, equivalent, or similar purpose It replaces.
In addition, it will be appreciated by those of skill in the art that although some embodiments in this include institute in other embodiments Including certain features rather than other feature, but the combination of the feature of different embodiment means in the scope of the present invention Within and form different embodiments.For example, in the following claims, embodiment claimed it is any it One can in any combination mode come using.
Various component embodiments of the invention can be implemented in hardware, or to run on one or more processors Software module realize, or be implemented in a combination thereof.It will be understood by those of skill in the art that can be used in practice In the forecasting system of microprocessor or digital signal processor (DSP) to realize active users according to an embodiment of the present invention Some or all components some or all functions.The present invention is also implemented as executing side as described herein Some or all device or device programs (for example, computer program and computer program product) of method.It is such It realizes that program of the invention can store on a computer-readable medium, or can have the shape of one or more signal Formula.Such signal can be downloaded from an internet website to obtain, and perhaps be provided on the carrier signal or with any other shape Formula provides.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and ability Field technique personnel can be designed alternative embodiment without departing from the scope of the appended claims.In the claims, Any reference symbol between parentheses should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not Element or step listed in the claims.Word "a" or "an" located in front of the element does not exclude the presence of multiple such Element.The present invention can be by means of including the hardware of several different elements and being come by means of properly programmed computer real It is existing.In the unit claims listing several devices, several in these devices can be through the same hardware branch To embody.The use of word first, second, and third does not indicate any sequence.These words can be explained and be run after fame Claim.
A1, a kind of forecasting system of active users, comprising:
Application layer, the input for obtaining user operates, and is operated based on the input and determine target date；
Computation layer, for predicting the target product in the mesh using the first prediction module and/or the second prediction module The active users on date are marked, prediction result is obtained；Wherein, first prediction module and second prediction module setting exist In the computation layer；First prediction module is tied for judging whether the target date is the specific date, and based on judgement Fruit predicts the active users of the target date；Second prediction module is for obtaining the target product in a upper week Historical user's number of phase, and predict based on historical user's number the active users of the target date；
Data Layer, for storing historical user's number and the prediction result；
The application layer is also used to obtain the prediction result from data Layer, and exports the prediction result.
A2, forecasting system as described in a1, first prediction module, are specifically used for:
Obtain the target date；Judge whether the target date is the specific date；If so, according to the particular day The estimation parameter and the target date corresponding time sequential value and its estimation parameter of phase, predicts enlivening for the target date Number of users.
A3, as described in A2 forecasting system, the specific date include one of following date or a variety of:
Weekend, the Spring Festival, National Day, other legal festivals and holidays.
A4, the forecasting system as described in A3, first prediction module, are specifically used for:
Based on DAU=a1*t+a2*xw+a3*xc+a4*xg+a5*xo+ C predicts the active users of the target date；Its In, DAU is the active users of the target date, and t is the time sequential value, a1Join for the estimation of the time sequential value Number；a2For the estimation parameter at weekend, when the target date is weekend, xW=1, when the target date is not weekend, xW =0；a3For the estimation parameter in the Spring Festival, when the target date is the Spring Festival, xc=1, when the target date is not the Spring Festival, xc=0；a4For the estimation parameter on National Day, when the target date is National Day, xg=1, it is not state in the target date When celebrating section, xg=0；a5For the estimation parameter in other legal red-letter days, when the target date is other legal red-letter days, xo=1, When the target date is not other legal red-letter days, xo=0；C is constant term.
A5, as described in A2 forecasting system, first prediction module, are also used to:
Obtain sample historical data；According to the sample historical data, estimation parameter and the institute of the specific date are determined State time sequential value estimation parameter.
A6, forecasting system as described in a5, first prediction module, are also used to:
Determine the earliest date of the data in the sample historical data；The target date is calculated from the earliest date The arrangement value risen, and using the arrangement value as the time sequential value.
A7, forecasting system as described in a1, second prediction module, are specifically used for:
Obtain target date from the application layer, the target date be it is to be predicted on the day of when, from the number The target product is obtained in historical user's number of upper a cycle according to layer；The day of estimating for obtaining the same day to be predicted Adds User Number；Based on historical user's number, old user's number on the same day to be predicted is determined；Day was estimated according to the same day to be predicted Add User several and the same day to be predicted old user's number, obtains same day active users to be predicted.
A8, the forecasting system as described in A7, second prediction module, are specifically used for:
When the target date is to be predicted the 1st day, obtain to be predicted the 1st day estimates the number that Adds User day； The several and described historical user's number that Adds User day was estimated according to the same day to be predicted, determines the new of to be predicted the 1st day Increase and retains number of users；According to old user's number on the same day to be predicted, determine that the 1st day old user to be predicted retains number； According to historical user's number, the 1st day reflux number of users to be predicted is determined；According to described 1st day to be predicted estimate Add User day number, the 1st day newly-increased retention number of users to be predicted, the 1st day old user retention to be predicted Several and the 1st day reflux number of users to be predicted, obtains the 1st day active users to be predicted.
A9, the forecasting system as described in A8, second prediction module, are specifically used for:
According to historical user's number, determine historical user's number in the 1st day reflux to product ratio to be predicted；Obtain institute State historical user's number and its in the product of the 1st day reflux to product ratio to be predicted, used as the reflux in the 1st day to be predicted Amount.
A10, the forecasting system as described in A8, second prediction module, are specifically used for:
When the target date is to be predicted the N days, it is based on historical user's number, determines to be predicted the N days Newly-increased retention number of users, the N days old users to be predicted retain number, the N days reflux numbers of users to be predicted and to Number of users is retained in reflux in the N days of prediction；Obtain to be predicted the N days estimates the number that Adds User day；According to described to be predicted Estimate the number that Adds User day, the N days newly-increased retention numbers of users to be predicted, the N days to be predicted described within the N days Old user retain number and described the N days to be predicted reflux numbers of users, the retention use of reflux in the N days to be predicted Amount predicts the N days day active users to be predicted, N >=2.
A11, the forecasting system as described in A10, second prediction module, are specifically used for:
Based on historical user's number, determines that old user's number on the same day to be predicted is old to be predicted the N days and use Family retention ratio；According to old user's number on the same day to be predicted and old user's number on the same day to be predicted in N to be predicted Its old user's retention ratio determines that the N days old users to be predicted retain number.
A12, the forecasting system as described in A10, second prediction module, are specifically used for:
Obtaining same day to be predicted to the N-1 days estimated the number that Adds User day；According to historical user's number, determine to Daily estimating Added User number day in the N days newly-increased retention ratios to be predicted in the same day to the N-1 days of prediction；According to institute The same day to be predicted to the N-1 days estimate is stated to Add User day number and its respectively in the N days newly-increased retention ratios to be predicted, Obtain the N days newly-increased retention numbers of users to be predicted.
A13, the forecasting system as described in A10, second prediction module, are specifically used for:
According to historical user's number, determine historical user's number in the N days reflux to product ratios to be predicted；According to institute It states historical user's number and its in the N days reflux to product ratios to be predicted, determines the N days reflux numbers of users to be predicted.
A14, the forecasting system as described in A10, second prediction module, are specifically used for:
To be predicted the 1st day is obtained to the N-1 days reflux users to be predicted；According to historical user's number, determine to Daily reflux user is in the N days reflux retention ratios to be predicted in the 1st day to the N-1 days of prediction；According to described to pre- Survey the 1st day to the N-1 days reflux users and its respectively in the N days reflux retention ratios to be predicted, determine described to be predicted Reflux in the N days retain number of users.
A15, the forecasting system as described in A14, second prediction module, are specifically used for:
According to historical user's number, determine historical user's number in i-th day reflux to product ratio to be predicted；Obtain institute State historical user's number and its in the product of i-th day reflux to product ratio to be predicted, used as the reflux in i-th day to be predicted Family, 1≤i≤N-1.
B16, a kind of prediction technique of active users, comprising:
The input operation of user is obtained, and is operated based on the input and determines target date；
The target product enlivening in the target date is predicted using the first prediction module and/or the second prediction module Number of users obtains prediction result；
Wherein, first prediction module is tied for judging whether the target date is the specific date, and based on judgement Fruit predicts the active users of the target date；Second prediction module is for obtaining the target product in a upper week Historical user's number of phase, and predict based on historical user's number the active users of the target date.
B17, the method as described in B16, it is described to predict the target product in the target day using the first prediction module The active users of phase obtain prediction result, comprising:
Obtain the target date；
Judge whether the target date is the specific date；
If so, according to the estimation parameter of the specific date and the target date corresponding time sequential value and its estimating Parameter is counted, predicts the active users of the target date.
B18, the method as described in B17, the specific date include one of following date or a variety of:
Weekend, the Spring Festival, National Day, other legal festivals and holidays.
B19, the method as described in B18, it is described corresponding according to the estimation parameter of the specific date and the target date Time sequential value and its estimation parameter, predict the active users of the target date, comprising:
Based on DAU=a1*t+a2*xw+a3*xc+a4*xg+a5*xo+ C predicts the active users of the target date；
Wherein, DAU is the active users of the target date, and t is the time sequential value, a1For the time series The estimation parameter of value；a2For the estimation parameter at weekend, when the target date is weekend, xW=1, the target date not When for weekend, xW=0；a3For the estimation parameter in the Spring Festival, when the target date is the Spring Festival, xc=1, in the target date When not being the Spring Festival, xc=0；a4For the estimation parameter on National Day, when the target date is National Day, xg=1, in the mesh When the mark date is not National Day, xg=0；a5It is other legal sections in the target date for the estimation parameter in other legal red-letter days When day, xo=1, when the target date is not other legal red-letter days, xo=0；C is constant term.
B20, the method as described in B17, the method also includes:
Obtain sample historical data；
According to the sample historical data, the estimation parameter and time sequential value estimation ginseng of the specific date are determined Number.
B21, the method as described in B20, the method also includes:
Determine the earliest date of the data in the sample historical data；
Arrangement value of the target date from the earliest date is calculated, and using the arrangement value as the time sequence Train value.
B22, the method as described in B16, second prediction module predict the target product in the target date Active users obtain prediction result, comprising:
Obtain the target date, the target date be it is to be predicted on the day of when, obtain the target product upper Historical user's number of a cycle；
Obtain the same day to be predicted estimates the number that Adds User day；
Based on historical user's number, old user's number on the same day to be predicted is determined；
According to the old user's number for estimating Add User day several and the same day to be predicted on the same day to be predicted, obtain Obtain same day active users to be predicted.
B23, the method as described in B22, second prediction module predict the target product in the target date Active users obtain prediction result, comprising:
When the target date is to be predicted the 1st day, obtain to be predicted the 1st day estimates the number that Adds User day；
The several and described historical user's number that Adds User day was estimated according to the same day to be predicted, determines to be predicted the 1 day newly-increased retention number of users；
According to old user's number on the same day to be predicted, determine that the 1st day old user to be predicted retains number；
According to historical user's number, the 1st day reflux number of users to be predicted is determined；
It is used according to several, the described newly-increased retention in 1st day to be predicted that Adds User day of estimating in the 1st day to be predicted Amount, the 1st day old user to be predicted retain number and the 1st day reflux number of users to be predicted, obtain to pre- The 1st day active users surveyed.
B24, the method as described in B23, it is described according to historical user's number, determine that reflux in the 1st day to be predicted is used Amount, comprising:
According to historical user's number, determine historical user's number in the 1st day reflux to product ratio to be predicted；
Obtain historical user's number and its 1st day reflux to product ratio to be predicted product, as described to be predicted The 1st day reflux number of users.
B25, the method as described in B23, it is described according to historical user's number, determine that reflux in the 1st day to be predicted is used Amount, comprising:
When the target date is to be predicted the N days, it is based on historical user's number, determines to be predicted the N days Newly-increased retention number of users, the N days old users to be predicted retain number, the N days reflux numbers of users to be predicted and to Number of users is retained in reflux in the N days of prediction；
Obtain to be predicted the N days estimates the number that Adds User day；
It is used according to several, the described newly-increased retention in the N days to be predicted that Adds User day of estimating in the N days to be predicted Amount, the N days old users to be predicted retain number and the N days reflux numbers of users to be predicted, it is described to Number of users is retained in reflux in the N days of prediction, predicts the N days day active users to be predicted, N >=2.
B26, the method as described in B25, it is described to be based on historical user's number, determine the N days old users to be predicted Retain number, comprising:
Based on historical user's number, determines that old user's number on the same day to be predicted is old to be predicted the N days and use Family retention ratio；
According to old user's number on the same day to be predicted and old user's number on the same day to be predicted to be predicted the N days Old user's retention ratio determines that the N days old users to be predicted retain number.
Method described in B27, B25, it is described to be based on historical user's number, determine newly-increased retention in the N days to be predicted Number of users, comprising:
Obtaining same day to be predicted to the N-1 days estimated the number that Adds User day；
According to historical user's number, determined in the same day to the N-1 days to be predicted and daily to estimate the number that Adds User day In the N days newly-increased retention ratios to be predicted；
It was Added User day number and its respectively in N to be predicted according to the same day to be predicted to the N-1 days estimate It newly-increased retention ratio obtains the N days newly-increased retention numbers of users to be predicted.
B28, the method as described in B25, it is described to be based on historical user's number, determine that reflux in the N days to be predicted is used Amount, comprising:
According to historical user's number, determine historical user's number in the N days reflux to product ratios to be predicted；
According to historical user's number and its in the N days reflux to product ratios to be predicted, determine described the N days to be predicted Reflux number of users.
B29, the method as described in B25, it is described to be based on historical user's number, determine that reflux in the N days to be predicted are retained Number of users, comprising:
To be predicted the 1st day is obtained to the N-1 days reflux users to be predicted；
According to historical user's number, determine reflux user daily in be predicted the 1st day to the N-1 days to be predicted The N days reflux retention ratios；
It is stayed according to the 1st day to the N-1 days reflux users to be predicted and its respectively in reflux in the N days to be predicted Rate is deposited, determines that number of users is retained in the reflux in the N days to be predicted.
B30, the method as described in B29, the method also includes:
According to historical user's number, determine historical user's number in i-th day reflux to product ratio to be predicted；Obtain institute State historical user's number and its in the product of i-th day reflux to product ratio to be predicted, used as the reflux in i-th day to be predicted Family, 1≤i≤N-1.
C31, a kind of computer readable storage medium, are stored thereon with computer program, when which is executed by processor Realize the method and step.

Claims (10)

1. a kind of forecasting system of active users characterized by comprising
Application layer, the input for obtaining user operates, and is operated based on the input and determine target date；
Computation layer, for predicting the target product in the target day using the first prediction module and/or the second prediction module The active users of phase obtain prediction result；Wherein, first prediction module and second prediction module are arranged described In computation layer；First prediction module is for judging whether the target date is the specific date, and it is pre- to be based on judging result Survey the active users of the target date；Second prediction module is for obtaining the target product in upper a cycle Historical user's number, and predict based on historical user's number the active users of the target date；
Data Layer, for storing historical user's number and the prediction result；
The application layer is also used to obtain the prediction result from data Layer, and exports the prediction result.
2. forecasting system as described in claim 1, which is characterized in that first prediction module is specifically used for:
Obtain the target date；Judge whether the target date is the specific date；If so, according to the specific date Estimate parameter and the target date corresponding time sequential value and its estimation parameter, predicts any active ues of the target date Number.
3. forecasting system as claimed in claim 2, which is characterized in that the specific date include one of following date or It is a variety of:
Weekend, the Spring Festival, National Day, other legal festivals and holidays.
4. forecasting system as claimed in claim 3, which is characterized in that first prediction module is specifically used for:
Based on DAU=a1*t+a2*xw+a3*xc+a4*xg+a5*xo+ C predicts the active users of the target date；Wherein, DAU is the active users of the target date, and t is the time sequential value, a1For the estimation parameter of the time sequential value； a2For the estimation parameter at weekend, when the target date is weekend, xW=1, when the target date is not weekend, xW= 0；a3For the estimation parameter in the Spring Festival, when the target date is the Spring Festival, xc=1, when the target date is not the Spring Festival, xc =0；a4For the estimation parameter on National Day, when the target date is National Day, xg=1, it is not National Day in the target date When section, xg=0；a5For the estimation parameter in other legal red-letter days, when the target date is other legal red-letter days, xo=1, When the target date is not other legal red-letter days, xo=0；C is constant term.
5. forecasting system as claimed in claim 2, which is characterized in that first prediction module is also used to:
Obtain sample historical data；According to the sample historical data, determine the specific date estimation parameter and it is described when Between sequential value estimate parameter.
6. forecasting system as claimed in claim 5, which is characterized in that first prediction module is also used to:
Determine the earliest date of the data in the sample historical data；The target date is calculated from the earliest date Arrangement value, and using the arrangement value as the time sequential value.
7. forecasting system as described in claim 1, which is characterized in that second prediction module is specifically used for:
Obtain target date from the application layer, the target date be it is to be predicted on the day of when, from the data Layer The target product is obtained in historical user's number of upper a cycle；Obtain the same day to be predicted estimates the number that Adds User day； Based on historical user's number, old user's number on the same day to be predicted is determined；According to the same day to be predicted to estimate day new The old user's number for adding amount and the same day to be predicted obtains same day active users to be predicted.
8. forecasting system as claimed in claim 7, which is characterized in that second prediction module is specifically used for:
When the target date is to be predicted the 1st day, obtain to be predicted the 1st day estimates the number that Adds User day；According to The same day to be predicted estimates the several and described historical user's number that Adds User day, determines that the newly-increased of to be predicted the 1st day is stayed Deposit number of users；According to old user's number on the same day to be predicted, determine that the 1st day old user to be predicted retains number；According to Historical user's number determines the 1st day reflux number of users to be predicted；According to described 1st day to be predicted to estimate day new Add amount, the 1st day newly-increased retention number of users to be predicted, the 1st day old user to be predicted retain number and The 1st day reflux number of users to be predicted, obtains the 1st day active users to be predicted.
9. a kind of prediction technique of active users characterized by comprising
The input operation of user is obtained, and is operated based on the input and determines target date；
Predict the target product in any active ues of the target date using the first prediction module and/or the second prediction module Number obtains prediction result；
Wherein, first prediction module is for judging whether the target date is the specific date, and it is pre- to be based on judging result Survey the active users of the target date；Second prediction module is for obtaining the target product in upper a cycle Historical user's number, and predict based on historical user's number the active users of the target date.
10. a kind of computer readable storage medium, is stored thereon with computer program, realized such as when which is executed by processor Method and step described in claim 9 claim.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109360031A (en) * 2018-11-16 2019-02-19 北京奇虎科技有限公司 A kind of prediction technique and device of active users
CN112116381A (en) * 2020-08-31 2020-12-22 北京基调网络股份有限公司 Moon life prediction method based on LSTM neural network, storage medium and computer equipment
CN112767028A (en) * 2021-01-20 2021-05-07 每日互动股份有限公司 Method for predicting number of active users, computer device and storage medium
CN112884503A (en) * 2021-01-21 2021-06-01 百果园技术(新加坡)有限公司 User scale prediction method, device, equipment and medium
CN116761185A (en) * 2023-08-21 2023-09-15 北京融信数联科技有限公司 Method, system and medium for predicting daily active users based on signaling

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109360031A (en) * 2018-11-16 2019-02-19 北京奇虎科技有限公司 A kind of prediction technique and device of active users
CN112116381A (en) * 2020-08-31 2020-12-22 北京基调网络股份有限公司 Moon life prediction method based on LSTM neural network, storage medium and computer equipment
CN112767028A (en) * 2021-01-20 2021-05-07 每日互动股份有限公司 Method for predicting number of active users, computer device and storage medium
CN112767028B (en) * 2021-01-20 2022-08-26 每日互动股份有限公司 Method for predicting number of active users, computer device and storage medium
CN112884503A (en) * 2021-01-21 2021-06-01 百果园技术(新加坡)有限公司 User scale prediction method, device, equipment and medium
CN116761185A (en) * 2023-08-21 2023-09-15 北京融信数联科技有限公司 Method, system and medium for predicting daily active users based on signaling

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