CN105869017A - Method and system for predicting ticket prices - Google Patents

Method and system for predicting ticket prices Download PDF

Info

Publication number
CN105869017A
CN105869017A CN201610187700.5A CN201610187700A CN105869017A CN 105869017 A CN105869017 A CN 105869017A CN 201610187700 A CN201610187700 A CN 201610187700A CN 105869017 A CN105869017 A CN 105869017A
Authority
CN
China
Prior art keywords
fare
date
model
forecast
pricing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201610187700.5A
Other languages
Chinese (zh)
Inventor
陈誉文
谭煜东
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Ctrip Business Co Ltd
Original Assignee
Shanghai Ctrip Business Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Ctrip Business Co Ltd filed Critical Shanghai Ctrip Business Co Ltd
Priority to CN201610187700.5A priority Critical patent/CN105869017A/en
Publication of CN105869017A publication Critical patent/CN105869017A/en
Pending legal-status Critical Current

Links

Classifications

    • 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
    • G06Q30/0283—Price estimation or determination
    • G06Q30/0284—Time or distance, e.g. usage of parking meters or taximeters

Abstract

The invention provides a method and a system for predicting ticket prices. The method comprises the following steps: acquiring all historical data of an object flight; extracting n time sequence ticket price data from the historical data so as to constitute n training samples; dividing the training samples into first training samples and second training samples; establishing basic predicting models which respectively correspond with the training samples, and training respective basic predicting model so as to determine respective parameter of the model; establishing a ticket price predicting model, introducing each second training sample to a corresponding basic predicting model for prediction so as to obtain n basic predicting values, and training the ticket price predicting model so as to determine respective parameter of the model; extracting n time sequence ticket price data so as to constitute n new training samples; introducing each new training sample to a corresponding basic predicting model for prediction so as to obtain n basic predicting values; introducing the acquired n basic predicting values to the ticket price predicting model for prediction so as to obtain a ticket price prediction value. The method can accurately predict ticket prices.

Description

Fare-pricing estimate method and system
Technical field
The present invention relates to a kind of fare-pricing estimate method and system, particularly to a kind of ticket based on machine learning Valency Forecasting Methodology and system.
Background technology
Along with the gradually development of aircraft industry, airline's great majority use complicated pricing strategy to improve it Income, thus cause the admission fee of aircraft to present high uncertainty, and due to price institute foundation Factors is not the most public domain, and ordinary consumer is often difficult to hold the alteration trend of price.Air ticket Price expectation, is the air ticket predicting some the given flight tool at following one or more time points Body price.Predict machine profile exactly, no matter budget is saved for consumer, or be that air ticket is sold Business holds market trends great forward facilitation.
Machine profile prediction is a typical recurrence learning problem, and the admission fee as output valve is a company The variable of ideotype.In current research with put into practice field, it is directed to theory and the side of machine profile prediction Method is less, and relevant research great majority concentrate in the optimisation strategy to air ticket purchase opportunity, i.e. a certain Individual given time point consumer should select to buy two classification problems being also to wait for.Existing method is also Great majority use the sorting algorithm in the middle of machine learning to build solution, and fresh few directly prediction is concrete Machine profile.Additionally, due to price expectation relates to multiple different flight and different dates, Modeling to price data is the most difficult.Therefore, the most feasible price expectation method still phase To scarcity.
The machine profile observed the most in the same time can be considered a time series.In time series analysis In, general employing moving average MA, autoregression model AR and difference slip autoregression model The methods such as ARIMA are predicted, but owing to these traditional method great majority assume history and following number According to linear, therefore it is not suitable for the prediction of the machine profile of Nonlinear Time Series.
Summary of the invention
The technical problem to be solved in the present invention is not possess feasible air ticket valency in prior art to overcome The defect of lattice Predicting Technique, it is provided that a kind of fare-pricing estimate method and system.
The present invention solves above-mentioned technical problem by following technical proposals:
The present invention provides a kind of fare-pricing estimate method, and its feature is, it comprises the following steps:
S1, gather all historical datas of a target order of classes or grades at school, each historical data includes this target order of classes or grades at school Start the date, apart from the natural law on this starting date and admission fee at that time；
S2, start on the basis of date starts by current, according to starting the date and/or distance starts the date The time sequencing of natural law from those historical datas, extract n time series fare data to constitute n Individual training sample；
S3, be the first training sample and the second training sample by each training sample random division；
S4, set up n and n training sample basic forecast model one to one, and to each base Plinth forecast model is trained determining the parameters in this basic forecast model, wherein, each base The input value of plinth forecast model be the first training sample that this basic forecast model is corresponding, output valve be current Date admission fee；
S5, set up a Model of Fare-Pricing Forecast, the basis that each second training sample substitutes into its correspondence is pre- Survey in model and predict to obtain n basic forecast value, and be trained determining to this Model of Fare-Pricing Forecast Parameters in this Model of Fare-Pricing Forecast, wherein, the input value of this Model of Fare-Pricing Forecast is this n base Plinth predictive value, output valve are current date admission fee on the same day；
S6, start the date by target and on the basis of this target starts the natural law on date, extract n Time series fare data is to constitute n new training sample；
S7, each new training sample is substituted into and the basic forecast model of its correspondence is predicted obtain n Basic forecast value；
S8, by step S7N basic forecast value of middle acquisition substitutes in this Model of Fare-Pricing Forecast to be predicted to obtain Obtain admission fee predictive value.
It is preferred that in step S2In, extract 5 time series fare data successively, be respectively as follows:
Laterally sequence fare data:D represents and currently starts the date, if i=0, Represent that the distance current starting date is admission fee when one；
Longitudinal sequence fare data: Represent current the previous day starting the date The admission fee on the same day；
Longitudinal sequence fare data: Before representing the current starting date The admission fee on the 7th day same day；
Longitudinal sequence fare data: Before representing the current starting date The admission fee on the 30th day same day；
Longitudinal sequence fare data: Represent current to start residing for the date The admission fee on the same day on correspondence date the previous year in time.
It is preferred that this basic forecast model uses K nearest neighbor algorithm to build.
It is preferred that this Model of Fare-Pricing Forecast uses linear regression model (LRM).
It is preferred that the data volume in this first training sample is not less than the data in this second training sample Amount.
The present invention also provides for a kind of fare-pricing estimate system, and its feature is, comprising:
One acquisition module, for gathering all historical datas of a target order of classes or grades at school, each historical data includes This target order of classes or grades at school start the date, apart from the natural law on this starting date and admission fee at that time；
One first abstraction module, on the basis of starting by current starting date, according to starting the date And/or the time sequencing of the natural law on distance starting date extracts n time sequence from those historical datas Row fare data is to constitute n training sample；
Stroke sub-module, being used for each training sample random division is the first training sample and the second instruction Practice sample；
One first training module, is used for setting up n and n training sample basic forecast mould one to one Type, and each basic forecast model is trained each ginseng determining in this basic forecast model Number, wherein, the input value of each basic forecast model is the first training that this basic forecast model is corresponding Sample, output valve are current date admission fee on the same day；
One second training module, is used for setting up a Model of Fare-Pricing Forecast, by each second training sample generation Enter and the basic forecast model of its correspondence is predicted to obtain n basic forecast value, and to this fare-pricing estimate mould Type is trained determining the parameters in this Model of Fare-Pricing Forecast, wherein, this Model of Fare-Pricing Forecast Input value be this n basic forecast value, output valve be current date admission fee on the same day；
One second abstraction module, for the natural law with the target starting date and apart from this target starting date be Benchmark, extracts n time series fare data to constitute n new training sample；
One first prediction module, for substituting into the basic forecast model of its correspondence by each new training sample Middle prediction is to obtain n basic forecast value；
One second prediction module, for calling n the basic forecast value generation that this first prediction module obtains Enter and this Model of Fare-Pricing Forecast is predicted obtain fare-pricing estimate value.
It is preferred that this first abstraction module is for extracting 5 time series fare data successively, respectively For:
Laterally sequence fare data:D represents and currently starts the date, if i=0, Represent that the distance current starting date is admission fee when one；
Longitudinal sequence fare data: Represent current the previous day starting the date The admission fee on the same day；
Longitudinal sequence fare data: Before representing the current starting date The admission fee on the 7th day same day；
Longitudinal sequence fare data: Before representing the current starting date The admission fee on the 30th day same day；
Longitudinal sequence fare data: Represent current to start residing for the date The admission fee on the same day on correspondence date the previous year in time.
It is preferred that this basic forecast model uses K nearest neighbor algorithm to build.
It is preferred that this Model of Fare-Pricing Forecast uses linear regression model (LRM).
It is preferred that the data volume in this first training sample is not less than the data in this second training sample Amount.
On the basis of meeting common sense in the field, above-mentioned each optimum condition, can combination in any, i.e. get Ben Fa Bright each preferred embodiments.
The most progressive effect of the present invention is:
The present invention proposes a kind of Data Modeling Method based on historic fare, is different from conventional time series Only considering the way of one-dimensional sequence in prediction, the present invention is by multiple different cycles such as year, the moon, week, days The time series extracted carries out integrated forecasting, thus avoids risk and the limitation of loss of learning, for machine Admission fee lattice provide a kind of practicable Forecasting Methodology, thus the present invention can go out target and rise by Accurate Prediction Dynamic date and the machine profile when this target starts the natural law on date.
Accompanying drawing explanation
Fig. 1 is that the sample of the flight prices table of present pre-ferred embodiments is shown.
Fig. 2 is the arrangement flow chart of the fare-pricing estimate method of present pre-ferred embodiments.
Detailed description of the invention
Further illustrate the present invention below by the mode of embodiment, but the most therefore limit the present invention to Among described scope of embodiments.
Embodiment of the present invention is classified into model training and two parts of fare-pricing estimate, wherein, the mesh of training Be to build available Model of Fare-Pricing Forecast, it follows the steps below:
Step 1: determine the target flight of prediction, and obtain this target flight institute to current date There is historical data.Such as, target flight is set to Beijing MU5137 the flight to Shanghai, current date Be JIUYUE in 2015 15, then historical data set should comprise all JIUYUE in 2015 15 days and The historgraphic data recording observed in the past, each historgraphic data recording comprises concrete taking off day Phase, apart from the natural law of this date and price at that time.
Step 2: work out the price list of this target flight on the basis of historical data set.Fig. 1 is The price list woven, in table, every string represents a fixing date, and every a line represents a spy Fixed distance take off before natural law, the machine profile that the value in cell then observes for this moment.By Fig. 1 Visible, owing to current date is JIUYUE in 2015 15, the flight historical price taken off in this day is Known quantity, and the flight taken off for 16th in JIUYUE 1 day and price earlier before take-off are also for known quantity, By that analogy.
Step 3: form training dataset.Make d represent currently and start the date, if i=0,Represent and work as The admission fee of the admission fee of front starting date, i.e. this flight JIUYUE in 2015 same day on the 15th is (in Fig. 1 653), for each known units lattice in price list, according to the different time cycles, extract successively Five time serieses:
1) horizontal sequence fare dataI.e. table is positioned atAll cells with on the right side of a line:
I i d = { p i + 1 d , p i + 2 d , ... }
2) longitudinal sequence fare dataI.e. table is positioned atAll cells with above string:
D i d = { p i d - 1 , p i d - 2 , ... }
3) longitudinal sequence fare dataI.e. table is positioned atWith owning with 7 as cycle above string Cell:
W i d = { p i d - 7 , p i d - 14 , ... }
4) longitudinal sequence fare dataI.e. table is positioned atWith above string with one month as cycle All cells, wherein, the length that each cycle crosses over should according to of that month total natural law, from 28 to Value in 31:
M i d = { p i d - 30 , p i d - 60 , . . . }
5) longitudinal sequence fare dataI.e. table is positioned atWith institute with 1 year as cycle above string Having cell, wherein, the length that each cycle crosses over should take 365 or 366 according to total natural law then:
Y i d = { p i d - 365 , p i d - 730 , ... }
In the price list of Fig. 1, as a example by the cell of 2015-09-15 row the 0th row, by different wire Five time series fare data are indicated.The fare data of each cell and extract five thereof Time series component constitutes five training samples.Significantly, since data are limited, time series Fare data can not infinitely extend, it is therefore necessary to limits each sequence queue size.In the application, each The sequence length that visual angle generates according to the size of data volume or should be determined by experiment.In this example, take Window size is 4, and the most each sequence comprises the fare data in 4 historical datas.
Step 4: as shown in Figure 2, is randomly divided into two parts by each training sample, the i.e. first instruction Practicing sample and the second training sample, those first training samples composing training collection a, training set a is used for instructing The basic forecast model that five time series components of white silk are corresponding, those second training samples composing training collection b, Training set b is used for training Model of Fare-Pricing Forecast, i.e. extra arbitration learner in Fig. 2.The ration of division Can determine according to concrete data volume, such as, can distribute training set by the proportion of 75% and 25% A and training set b.
Step 5: generate five basic forecast model f1,f2,f3,f4,f5, these five basic forecast models are respectively With five the first training sample one_to_one corresponding.Use training set a generated in step 4 that it is carried out respectively Training is to determine the parameters in this basic forecast model, as shown in the solid arrow in Fig. 2, each The input value of individual basic forecast model is that the first training sample that this basic forecast model is corresponding, output valve are Current date admission fee on the same day.In this example, the K nearest neighbor algorithm (KNN) in machine learning is used Being used as basic forecast model, each KNN model is respectively acting onWhen five Between in sequence one.
It is pointed out that KNN algorithm the most merely illustrative, in practical operation, any can Machine learning algorithm or other models of predicted time sequence can be replaced in the framework of this method, Therefore, its equivalent form of value also should be protected according to the claim detailed rules and regulations in the present invention.
Step 6: generate an extra arbitration learner, i.e. Model of Fare-Pricing Forecast, and with in step 4 It is trained by training set b generated.Dotted portion in Fig. 2 illustrates the process of this step, first First, for the second training sample of each in training set b, five basic forecast moulds all it are inputted to Type is to obtain five different basic forecast values.Secondly, using five basic forecast values as arbitration learner Input, it, as learning target, is further trained by current date admission fee on the same day.In this example In, this arbitration learner has selected linear regression model (LRM), and it is substantially the prediction for five basic models Value imparts different weights, draws final predicting the outcome by weighting.
After above step, it is possible to obtain five the basic forecast models trained and one secondary Cut out learner, at this point it is possible to the blank cell of lower section in price list is predicted.
Concretely comprising the following steps of prediction:
Step 7: determine prediction target.Such as, target flight is that JIUYUE in 2015 is taken off on the 25th MU5137 flight, current date is JIUYUE 15 days (distance is taken off first 10 days) in 2015, it was predicted that Target is this flight price JIUYUE (distance is taken off first 9 days) on the 16th.
Step 8: for the cell of d=2015-09-25 in price list, i=9, on the basis of it, press Five time series characteristic components of correspondence are extracted according to the method in step 3With Constitute five new training samples.
Step 9: five new training samples are flowed to respectively the basic forecast model of correspondence, obtains five Basic forecast value.
Step 10: flow to five basic forecast values arbitrate learner, it is thus achieved that final predicts the outcome i.e. Fare-pricing estimate value.
The present embodiment proposes a kind of data modeling based on price list and fare-pricing estimate method, greatly simplifies The most complicated machine profile data represent, also the different price observation of order in time interrelated Clearly presented.It is different from the way only considering one-dimensional sequence in conventional time series prediction, this enforcement The time series that example is extracted by multiple different cycles such as year, the moon, week, days carries out integrated forecasting, from And avoid risk and the limitation of loss of learning, provide a kind of practicable prediction side for machine profile Method.
Although the foregoing describing the detailed description of the invention of the present invention, but those skilled in the art should managing Solving, these are merely illustrative of, and protection scope of the present invention is defined by the appended claims.This The technical staff in field, can be to these embodiment party on the premise of without departing substantially from the principle of the present invention and essence Formula makes various changes or modifications, but these changes and amendment each fall within protection scope of the present invention.

Claims (10)

1. a fare-pricing estimate method, it is characterised in that it comprises the following steps:
S1, gather all historical datas of a target order of classes or grades at school, each historical data includes this target order of classes or grades at school Start the date, apart from the natural law on this starting date and admission fee at that time；
S2, start on the basis of date starts by current, according to starting the date and/or distance starts the date The time sequencing of natural law from those historical datas, extract n time series fare data to constitute n Individual training sample；
S3, be the first training sample and the second training sample by each training sample random division；
S4, set up n and n training sample basic forecast model one to one, and to each base Plinth forecast model is trained determining the parameters in this basic forecast model, wherein, each base The input value of plinth forecast model be the first training sample that this basic forecast model is corresponding, output valve be current Date admission fee；
S5, set up a Model of Fare-Pricing Forecast, the basis that each second training sample substitutes into its correspondence is pre- Survey in model and predict to obtain n basic forecast value, and be trained determining to this Model of Fare-Pricing Forecast Parameters in this Model of Fare-Pricing Forecast, wherein, the input value of this Model of Fare-Pricing Forecast is this n base Plinth predictive value, output valve are current date admission fee on the same day；
S6, start the date by target and on the basis of this target starts the natural law on date, extract n Time series fare data is to constitute n new training sample；
S7, each new training sample is substituted into and the basic forecast model of its correspondence is predicted obtain n Basic forecast value；
S8, by step S7N basic forecast value of middle acquisition substitutes in this Model of Fare-Pricing Forecast to be predicted to obtain Obtain admission fee predictive value.
2. fare-pricing estimate method as claimed in claim 1, it is characterised in that in step S2In, depend on Secondary extract 5 time series fare data, be respectively as follows:
Laterally sequence fare data:D represents and currently starts the date, if i=0, Represent that the distance current starting date is admission fee when one；
Longitudinal sequence fare data: Represent current the previous day starting the date The admission fee on the same day；
Longitudinal sequence fare data: Before representing the current starting date The admission fee on the 7th day same day；
Longitudinal sequence fare data: Before representing the current starting date The admission fee on the 30th day same day；
Longitudinal sequence fare data: Represent current to start residing for the date The admission fee on the same day on correspondence date the previous year in time.
3. fare-pricing estimate method as claimed in claim 1, it is characterised in that this basic forecast model K nearest neighbor algorithm is used to build.
4. fare-pricing estimate method as claimed in claim 1, it is characterised in that this Model of Fare-Pricing Forecast Use linear regression model (LRM).
5. fare-pricing estimate method as claimed in claim 1, it is characterised in that this first training sample In data volume not less than the data volume in this second training sample.
6. a fare-pricing estimate system, it is characterised in that comprising:
One acquisition module, for gathering all historical datas of a target order of classes or grades at school, each historical data includes This target order of classes or grades at school start the date, apart from the natural law on this starting date and admission fee at that time；
One first abstraction module, on the basis of starting by current starting date, according to starting the date And/or the time sequencing of the natural law on distance starting date extracts n time sequence from those historical datas Row fare data is to constitute n training sample；
Stroke sub-module, being used for each training sample random division is the first training sample and the second instruction Practice sample；
One first training module, is used for setting up n and n training sample basic forecast mould one to one Type, and each basic forecast model is trained each ginseng determining in this basic forecast model Number, wherein, the input value of each basic forecast model is the first training that this basic forecast model is corresponding Sample, output valve are current date admission fee on the same day；
One second training module, is used for setting up a Model of Fare-Pricing Forecast, by each second training sample generation Enter and the basic forecast model of its correspondence is predicted to obtain n basic forecast value, and to this fare-pricing estimate mould Type is trained determining the parameters in this Model of Fare-Pricing Forecast, wherein, this Model of Fare-Pricing Forecast Input value be this n basic forecast value, output valve be current date admission fee on the same day；
One second abstraction module, for the natural law with the target starting date and apart from this target starting date be Benchmark, extracts n time series fare data to constitute n new training sample；
One first prediction module, for substituting into the basic forecast model of its correspondence by each new training sample Middle prediction is to obtain n basic forecast value；
One second prediction module, for calling n the basic forecast value generation that this first prediction module obtains Enter and this Model of Fare-Pricing Forecast is predicted obtain fare-pricing estimate value.
7. fare-pricing estimate system as claimed in claim 6, it is characterised in that this first abstraction module For extracting 5 time series fare data successively, it is respectively as follows:
Laterally sequence fare data:D represents and currently starts the date, if i=0, Represent that the distance current starting date is admission fee when one；
Longitudinal sequence fare data: Represent current the previous day starting the date The admission fee on the same day；
Longitudinal sequence fare data: Before representing the current starting date The admission fee on the 7th day same day；
Longitudinal sequence fare data: Before representing the current starting date The admission fee on the 30th day same day；
Longitudinal sequence fare data: Represent current to start residing for the date The admission fee on the same day on correspondence date the previous year in time.
8. fare-pricing estimate system as claimed in claim 6, it is characterised in that this basic forecast model K nearest neighbor algorithm is used to build.
9. fare-pricing estimate system as claimed in claim 6, it is characterised in that this Model of Fare-Pricing Forecast Use linear regression model (LRM).
10. fare-pricing estimate system as claimed in claim 6, it is characterised in that this first training sample In data volume not less than the data volume in this second training sample.
CN201610187700.5A 2016-03-29 2016-03-29 Method and system for predicting ticket prices Pending CN105869017A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610187700.5A CN105869017A (en) 2016-03-29 2016-03-29 Method and system for predicting ticket prices

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610187700.5A CN105869017A (en) 2016-03-29 2016-03-29 Method and system for predicting ticket prices

Publications (1)

Publication Number Publication Date
CN105869017A true CN105869017A (en) 2016-08-17

Family

ID=56625325

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610187700.5A Pending CN105869017A (en) 2016-03-29 2016-03-29 Method and system for predicting ticket prices

Country Status (1)

Country Link
CN (1) CN105869017A (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107767094A (en) * 2017-09-22 2018-03-06 北京京东尚科信息技术有限公司 A kind of logistics ladder freight charges optimization method and device
CN109472399A (en) * 2018-10-23 2019-03-15 上海交通大学 Consider the air ticket purchase decision method and system of uncertainty in traffic
CN110111036A (en) * 2019-03-28 2019-08-09 跨越速运集团有限公司 Logistics goods amount prediction technique and system based on LSTM Model Fusion
CN111091407A (en) * 2019-10-28 2020-05-01 海南太美航空股份有限公司 Airline passenger seat rate prediction method and system
CN112132323A (en) * 2020-08-25 2020-12-25 汉海信息技术(上海)有限公司 Method and device for predicting value amount of commodity object and electronic equipment
CN112767035A (en) * 2021-01-25 2021-05-07 海南太美航空股份有限公司 Flight fare estimation method, system and electronic equipment

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107767094A (en) * 2017-09-22 2018-03-06 北京京东尚科信息技术有限公司 A kind of logistics ladder freight charges optimization method and device
CN109472399A (en) * 2018-10-23 2019-03-15 上海交通大学 Consider the air ticket purchase decision method and system of uncertainty in traffic
CN110111036A (en) * 2019-03-28 2019-08-09 跨越速运集团有限公司 Logistics goods amount prediction technique and system based on LSTM Model Fusion
CN111091407A (en) * 2019-10-28 2020-05-01 海南太美航空股份有限公司 Airline passenger seat rate prediction method and system
CN112132323A (en) * 2020-08-25 2020-12-25 汉海信息技术(上海)有限公司 Method and device for predicting value amount of commodity object and electronic equipment
CN112767035A (en) * 2021-01-25 2021-05-07 海南太美航空股份有限公司 Flight fare estimation method, system and electronic equipment

Similar Documents

Publication Publication Date Title
CN105869017A (en) Method and system for predicting ticket prices
CN107895283B (en) Merchant passenger flow volume big data prediction method based on time series decomposition
CN106909933B (en) A kind of stealing classification Forecasting Methodology of three stages various visual angles Fusion Features
Xie et al. Variable selection methods for probabilistic load forecasting: Empirical evidence from seven states of the united states
CN103729689B (en) Power grid electric quantity prediction method based on industry classifications and leading industry data
CN107844915A (en) A kind of automatic scheduling method of the call center based on traffic forecast
CN105023066B (en) A kind of Business Process System analysing and predicting system and method based on seasonal adjustment
CN109214719A (en) A kind of system and method for the marketing inspection analysis based on artificial intelligence
CN111062757A (en) Information recommendation method and system based on multi-path optimization matching
CN103310286A (en) Product order prediction method and device with time series characteristics
CN102541920A (en) Method and device for improving accuracy degree by collaborative filtering jointly based on user and item
CN112700027A (en) Multi-precision index comprehensive evaluation method and system for electric power prediction model
CN108388974A (en) Top-tier customer Optimum Identification Method and device based on random forest and decision tree
CN107909392A (en) Stimulation behavior susceptibility Forecasting Methodology, device, equipment and readable storage medium storing program for executing
CN105912686A (en) Search engine marketing bid method and system based on machine learning
CN106779478A (en) A kind of load scheduling Valuation Method
CN104346698A (en) Catering member big data analysis and checking system based on cloud computing and data mining
CN108846695A (en) The prediction technique and device of terminal replacement cycle
CN110458668A (en) Determine the method and device of Products Show algorithm
CN108154311A (en) Top-tier customer recognition methods and device based on random forest and decision tree
CN107679103A (en) For entity attributes analysis method and system
Ripoll-Zarraga et al. Exploring the reasons for efficiency in Spanish airports
CN114782065A (en) Commodity sales volume prediction method and device based on model combination and storage medium
CN114239292B (en) Comprehensive evaluation method and system for low-carbon economic operation-oriented multifunctional demand park
CN104794322B (en) Solar cell module limited buffer multiple target lot size scheduling method based on DNSGA II

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20160817