CN111538955A - Goods sales prediction method, device and storage medium - Google Patents
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Abstract
The present disclosure relates to a method for predicting goods sales, applied to an electronic device, comprising: acquiring historical sales data of goods to be predicted; determining a prediction parameter according to the historical sales data; and obtaining the predicted sales volume of the given month through a linear regression model according to the determined prediction parameters. According to the method for predicting the goods sales volume, the prediction parameters are determined according to the historical sales volume data, the predicted sales volume of the given month is obtained through the linear regression model, and the sales volume of the given month can be accurately predicted for reference.
Description
Technical Field
The present disclosure relates to the field of computer processing, and in particular, to a method, an apparatus, and a storage medium for predicting sales of goods.
Background
In the related art, when a sales department predicts future sales volume, sales personnel often make a future annual sales plan according to personal experience, and then redistribute the sales plan to each month, and then redistribute the sales plan to each day according to monthly distribution results. The formulation of such sales plans is extremely dependent on the personal experience of the sales staff, and the sales staff maps the annual plans to the monthly plans, which are mapped to the daily plans, the weight in the annual plan for each month and the weight in the monthly plan for each day are generally fixed, and less change, so the flexibility is poor. The problem to be solved is to provide a method for predicting the sales volume of goods close to the real sales data.
Disclosure of Invention
To overcome the problems in the related art, the present disclosure provides a method, an apparatus, and a storage medium for predicting sales of goods.
According to a first aspect of the embodiments of the present disclosure, there is provided a method for predicting sales of goods, the method being applied to an electronic device, including:
acquiring historical sales data of goods to be predicted;
determining a prediction parameter according to the historical sales data;
and obtaining the predicted sales volume of the given month through a linear regression model according to the determined prediction parameters.
Wherein obtaining the predicted sales volume for the given month by the linear regression model based on the determined prediction parameters comprises:
acquiring parameter values and weight values of the prediction parameters;
and obtaining the predicted sales volume of the given month through the linear regression model according to the parameter values and the weight values of the prediction parameters.
Wherein obtaining the predicted sales volume for the given month by the linear regression model according to the parameter values and the weight values of the prediction parameters comprises:
obtaining the product of the parameter value and the weight value of the prediction parameter;
and summing the products of the parameter values and the weight values of all the prediction parameters to obtain the predicted sales volume of the given month.
Wherein the prediction parameters include one or more of the following parameters:
the mean value of the same-ratio sales volume, the mean value of the ring-ratio sales volume, the variance of the historical sales data, the poisson distribution value of the historical sales volume data, the mean value of the historical sales volume data, and the slope value of the historical sales volume data;
wherein the slope value of the historical sales data is a value of an absolute value of a difference of sales data of a latest month minus sales data of an earliest month in the historical sales data divided by a total number of months in which the historical sales data is counted.
When the prediction parameter comprises a poisson distribution value of the historical sales volume data, the poisson distribution value of the historical sales volume data is obtained according to the following mode:
acquiring a mean value and a confidence value of historical sales data in a first preset period;
and acquiring a Poisson distribution value of the historical sales data according to the acquired mean value and the confidence value of the historical sales data in the first preset period.
Wherein, the prediction method further comprises obtaining an optimal confidence value;
the obtaining the optimal confidence value comprises:
selecting T confidence values in a preset interval, wherein T is a positive integer greater than or equal to 1;
calculating a poisson distribution value corresponding to each confidence value;
and determining an optimal confidence value through a prediction model according to the calculated Poisson distribution value.
Wherein the prediction method further comprises:
determining a weight value of the prediction parameter;
the determining the weight values of the prediction parameters comprises:
determining sales data of each month in the historical sales data;
determining a prediction parameter value for each prediction parameter in each month;
and determining the weight value of each prediction parameter according to the sales data of each month and the prediction parameter value of each prediction parameter in each month.
Wherein the prediction method further comprises:
the weight values for each prediction parameter are corrected using an L2 regularized penalty function.
Wherein the obtaining of the historical sales data of the goods to be forecasted comprises:
and acquiring historical sales data of the goods to be predicted in a second preset period.
Wherein the prediction method further comprises:
obtaining a sales volume adjustment factor for each day in the given month;
determining the sales volume per day in the given month based on the sales volume adjustment coefficient.
Wherein the obtaining of the sales volume adjustment factor for each day in the given month comprises:
determining a category for each day in the given month;
determining a sales volume adjustment factor for each day in the given month according to the determined category of each day in the given month.
Wherein the prediction method further comprises:
updating the daily sales adjustment coefficient for the given month according to a preset period.
According to a first aspect of the embodiments of the present disclosure, there is provided an apparatus for predicting sales of goods, including:
the acquisition module is configured to acquire historical sales data of goods to be predicted;
a determination module configured to determine a prediction parameter from the historical sales data;
a prediction module configured to obtain a predicted sales volume for the given month via a linear regression model based on the determined prediction parameters.
Wherein the prediction module is configured to:
acquiring parameter values and weight values of the prediction parameters;
and obtaining the predicted sales volume of the given month through the linear regression model according to the parameter values and the weight values of the prediction parameters.
Wherein the prediction module is configured to:
obtaining the product of the parameter value and the weight value of the prediction parameter;
and summing the products of the parameter values and the weight values of all the prediction parameters to obtain the predicted sales volume of the given month.
Wherein the prediction parameters include one or more of the following parameters:
the mean value of the same-ratio sales volume, the mean value of the ring-ratio sales volume, the variance of the historical sales data, the poisson distribution value of the historical sales volume data, the mean value of the historical sales volume data, and the slope value of the historical sales volume data;
wherein the slope value of the historical sales data is a value of an absolute value of a difference of sales data of a latest month minus sales data of an earliest month in the historical sales data divided by a total number of months in which the historical sales data is counted.
Wherein, when the prediction parameter comprises a poisson distribution value of the historical sales data, the determination module is configured to:
acquiring a mean value and a confidence value of historical sales data in a first preset period;
and acquiring a Poisson distribution value of the historical sales data according to the acquired mean value and the confidence value of the historical sales data in the first preset period.
Wherein the prediction apparatus further comprises an optimal confidence value acquisition module configured to:
selecting T confidence values in a preset interval, wherein T is a positive integer greater than or equal to 1;
calculating a poisson distribution value corresponding to each confidence value;
and determining an optimal confidence value through a prediction model according to the calculated Poisson distribution value.
Wherein the prediction apparatus further comprises a weight value determination module configured to:
determining sales data of each month in the historical sales data;
determining a prediction parameter value for each prediction parameter in each month;
and determining the weight value of each prediction parameter according to the sales data of each month and the prediction parameter value of each prediction parameter in each month.
Wherein the prediction apparatus further comprises:
a correction module configured to correct the weight value of each prediction parameter using an L2 regularization penalty function.
Wherein the acquisition module is configured to:
and acquiring historical sales data of the goods to be predicted in a second preset period.
Wherein the prediction apparatus further comprises:
a daily sales adjustment coefficient acquisition module configured to acquire a daily sales adjustment coefficient in the given month;
a daily sales determination module configured to determine a daily sales in the given month based on the sales adjustment coefficient.
Wherein the daily sales adjustment coefficient acquisition module is configured to:
determining a category for each day in the given month;
determining a sales volume adjustment factor for each day in the given month according to the determined category of each day in the given month.
Wherein the prediction apparatus further comprises:
an updating module configured to update the sales volume adjustment coefficient for each day in the given month according to a preset cycle.
According to a third aspect of the embodiments of the present disclosure, there is provided an apparatus for predicting sales of goods, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring historical sales data of goods to be predicted;
determining a prediction parameter according to the historical sales data;
and obtaining the predicted sales volume of the given month through a linear regression model according to the determined prediction parameters.
According to a fourth aspect of embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium having instructions stored thereon, which when executed by a processor of a mobile terminal, enable the mobile terminal to perform a method of predicting an amount of goods sold, the method comprising:
acquiring historical sales data of goods to be predicted;
determining a prediction parameter according to the historical sales data;
and obtaining the predicted sales volume of the given month through a linear regression model according to the determined prediction parameters.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects: by determining the prediction parameters according to the historical sales data and obtaining the predicted sales volume of the given month through the linear regression model, the sales volume of the given month can be accurately predicted for reference.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow diagram illustrating a method for forecasting sales of an item according to an exemplary embodiment;
FIG. 2 is a flow chart of a method for obtaining the predicted sales volume for a given month by a linear regression model according to the determined prediction parameters in step S13 in FIG. 1;
FIG. 3 is a flowchart of a method for obtaining the predicted sales volume for a given month through a linear regression model according to the parameter values and the weight values of the prediction parameters in step S132 of FIG. 2;
FIG. 4 illustrates types of prediction parameters;
FIG. 5 illustrates a flow chart of a method of obtaining a Poisson distribution value of historical sales data;
FIG. 6 illustrates a flow chart of a method of determining confidence values shown in an exemplary embodiment of the present disclosure;
FIG. 7 illustrates a flow chart of a method for weight value determination of prediction parameters in accordance with an exemplary embodiment of the present disclosure;
FIG. 8 illustrates a flow chart of a method for weight value correction of prediction parameters in accordance with an exemplary embodiment of the present disclosure;
FIG. 9 is a flowchart illustrating a method of obtaining historical sales data for the item to be forecasted in step S11 shown in FIG. 1;
an exemplary illustrative method of forecasting sales of a good according to the present disclosure is shown in FIG. 10;
FIG. 11 is a flowchart illustrating a method of obtaining the daily sales adjustment coefficient for a given month in step S14 of FIG. 10;
a flowchart illustrating a method of forecasting sales of an item according to an exemplary embodiment of the present disclosure is shown in fig. 12;
FIG. 13 is a block diagram illustrating an apparatus for forecasting sales of goods in accordance with an exemplary embodiment;
fig. 14 is a block diagram illustrating a prediction apparatus of an item sales amount according to an exemplary embodiment (a general structure of a mobile terminal).
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
The embodiment of the disclosure provides a method for predicting goods sales volume. Fig. 1 is a flowchart illustrating a method for forecasting sales of an item, according to an exemplary embodiment, and the method for forecasting sales of an item, as shown in fig. 1, is used in an electronic device and includes the following steps.
Step S11, obtaining the historical sales data of the goods to be forecasted.
In step S12, a prediction parameter is determined based on the historical sales data.
And step S13, obtaining the predicted sales volume of the given month through a linear regression model according to the determined prediction parameters.
The method provided by the disclosure is applied to electronic equipment such as a mobile phone, a tablet computer and the like. According to the method for predicting the sales volume of the goods, the prediction parameters are determined according to the historical sales volume data of the goods to be predicted, and the predicted sales volume of the given month is obtained through the linear regression model according to the prediction parameters. The forecast parameters are parameters related to forecast sales, namely factors influencing the sales of the goods.
The historical sales data may be historical sales data for a predetermined period of time prior to a given month. For example, data for the first 3 months of a given month or historical sales data for the first 6 months.
According to the method for predicting the goods sales volume, the prediction parameters are determined according to the historical sales volume data, the predicted sales volume of the given month is obtained through the linear regression model, and the sales volume of the given month can be accurately predicted for reference.
The embodiment of the disclosure provides a method for predicting goods sales volume. As shown in fig. 2, fig. 2 shows a flowchart of a method for obtaining the predicted sales volume of a given month through a linear regression model according to the determined prediction parameters in step S13 in fig. 1:
in step S131, the parameter values and weight values of the prediction parameters are acquired.
In step S132, the predicted sales volume for the given month is obtained by the linear regression model according to the parameter values and the weight values of the prediction parameters.
When the prediction parameters are multiple, a weight can be given to each prediction parameter, and the predicted sales volume of a given month is obtained through a linear regression model according to the parameter values of the prediction parameters and the corresponding weight values. By giving the weight value of each prediction parameter, the influence and contribution of each prediction parameter to the predicted sales volume of a given month can be accurately determined, and the prediction accuracy is improved.
The embodiment of the disclosure provides a method for predicting goods sales volume. As shown in fig. 3, fig. 3 shows a flowchart of a method for obtaining the predicted sales volume of a given month by a linear regression model according to the parameter values and the weight values of the prediction parameters in step S132 in fig. 2:
in step S1321, the product of the parameter value and the weight value of the prediction parameter is acquired.
In step S1322, the products of the parameter values and the weight values of all the prediction parameters are summed to obtain the predicted sales volume for the given month.
In the present disclosure, a prediction of predicted sales is made for a given month using a linear regression model. The linear regression model predicts the parameter values of the parameters in a linear relationship with the predicted sales for a given month. In the present disclosure, the parameter value of each prediction parameter is multiplied by its corresponding weight value, and the products of the parameter values of all prediction parameters and their corresponding weight values are added together to obtain the predicted sales volume for a given month. For example, if there are three predictive parameters, a1, a2 and a3, with weighting values of w1, w2 and w3, respectively, then the predicted sales volume for a given month is a1 w1+ a2 w2+ a3 w 3.
The embodiment of the disclosure provides a method for predicting goods sales volume. As shown in fig. 4, fig. 4 shows the types of prediction parameters:
in step 121, the prediction parameters include one or more of the following parameters:
the mean value of the same-ratio sales volume, the mean value of the ring-ratio sales volume, the variance of historical sales data, the Poisson distribution value of the historical sales volume data, the mean value of the historical sales volume data and the slope value of the historical sales volume data;
wherein the slope value of the historical sales data is a value of an absolute value of a difference of the sales data of the latest month minus the sales data of the earliest month in the historical sales data divided by a total number of months of the statistical historical sales data.
The average value of the sales in the same scale is the average value of the sum of sales in the same month as the given month in the previous n years, and n is a positive integer ≧ 1. For example, if a given month is april, the average of the quantity of sales of april of the previous 3 years for month 4 may be the average of the sum of the quantity of sales of april of the previous year, or the quantity of sales of april of the previous year may be taken as the average of the quantity of sales of the april of the previous year.
The average value of the ring specific pin values is the pin value of the previous m months, and m is a positive integer not less than 1. For example, if a given month is April, the average value of the ring ratio pin amounts may be an average value of the sum of the pin amounts of February and March, and the pin amount value of March may also be taken as the average value of the ring ratio pin amounts.
The variance of the historical sales data is a value obtained by calculating the variance of the historical sales data. The variance is used for representing the dispersion degree of the sales data, the dispersion degree is large when the variance value is large, and the dispersion degree is small when the variance value is small. Any variance algorithm that can characterize the degree of dispersion of the sales data can be used in the methods provided in the present disclosure, and is not limited herein.
The average of the historical sales data refers to the average of the historical sales data. For example, the historical sales data is historical sales data for 6 months prior to a given month, and the average of the historical sales data may be a value obtained by dividing the sum of the historical sales data for 6 months prior to the given month by 6.
The slope value of the historical sales data is a value of the absolute value of the difference between the sales data of the latest month minus the sales data of the earliest month in the historical sales data divided by the total number of months of the statistical historical sales data. For example, the historical sales data is historical sales data for 6 months prior to a given month, and the slope value for the historical sales data for april may be the absolute value of the difference between the sales data for march minus the sales data for the previous 10 months of the year divided by 6.
The poisson distribution value of the historical sales data is a value obtained by calculating poisson distribution of the historical sales data. The poisson distribution is a calculation of the discrete probability of historical sales data.
The embodiment of the disclosure provides a method for predicting goods sales volume. As shown in fig. 5, fig. 5 shows a flowchart of a method for obtaining poisson distribution values for historical sales data:
in step S1211, a mean value and a confidence value of the historical sales data in the first preset period are obtained.
In step S1212, a poisson distribution value of the historical sales data is obtained according to the obtained mean value and confidence value of the historical sales data in the first preset period.
In the disclosure, a poisson distribution value of historical sales data may be obtained according to a mean value of the historical sales data and a given confidence value in a first preset period. For example, if the first predetermined period is three months and the given confidence value is 0.9, the poisson distribution value may be calculated according to the following formula:
wherein λ is historical sales data, K ═ 0,1,2,3, …;
the sum of the values of P is calculated by substituting the values of K into the above equation in order starting from 0. When the sum of the values of P is 0.9 or more, the value of K is a Poisson distribution value. For example, a pair of P1 obtained by substituting K0 into the above formula gives P0 and K1 into the formula gives P0+ P1+ …, and when the sum is 0.9 or more, the K value is a poisson distribution value. For example, if K is 2, P0+ P1+ P2 ≧ 0.9, then the poisson distribution value is 2.
The confidence value may be a human-specified value or a preset value. To be able to determine the optimal confidence value, the process can be performed as follows. As shown in fig. 6, fig. 6 shows a flowchart of a method for determining a confidence value shown in an exemplary embodiment of the present disclosure:
in step S12111, T confidence values are selected within a preset interval, where T is a positive integer greater than or equal to 1.
In step S12112, a poisson distribution value corresponding to each confidence value is calculated.
In step S12113, an optimal confidence value is determined by the predictive model based on the calculated poisson distribution value.
In the present disclosure, for determining the optimal confidence value, one region of confidence values may be given, for example, the confidence value may be selected between 0.7-1.0, and then T confidence values, for example, 30, may be selected from the confidence values between 0.7-1.0. The 30 confidence values may be chosen uniformly between 0.7 and 1.0, or may be chosen in other ways. And calculating corresponding Poisson distribution values according to the 30 confidence values, inputting all calculated Poisson distribution values into a prediction model, and determining an optimal confidence value. The prediction model may be any model that can obtain an optimal confidence value, and may be, for example, a Grid Search (Grid Search) prediction model.
The present disclosure provides a method for predicting goods sales, as shown in fig. 7, fig. 7 shows a flowchart of a method for determining weight values of prediction parameters according to an exemplary embodiment of the present disclosure:
in step S1311, sales data per month in the historical sales data is determined.
In step S1312, a prediction parameter value of each prediction parameter in each month is determined.
In step S1313, a weight value of each prediction parameter is determined based on the sales data for each month and the prediction parameter value of each prediction parameter in each month.
In the present disclosure, the weight value of each prediction parameter may be determined according to the sales data of each month in the historical sales data and the parameter value of each prediction parameter in each month. For example, given a month of april, the prediction parameters include three prediction parameters, a1, a2, and a3, with corresponding weight values of w1, w2, and w 3. If the historical sales data is sales data for three months prior to the given month, then the predicted parameter values for the predicted parameters for the month of three are a31, a32, a 33; the predicted parameter values of the predicted parameters for month february are a21, a22, a 23; the predicted parameter values for the predicted parameters for month of january are a11, a12, a 13.
The value of the march sales data is a31 w1+ a32 w2+ a33 w 3;
the value of the sales data for february, a21 w1+ a22 w2+ a23 w 3;
the value of sales data for january is a11 w1+ a12 w2+ a13 w 3.
The sales data of January, February and March can be obtained through historical sales data, and the corresponding prediction parameter value of the March can also be obtained according to the historical sales data. From the above three formulas, the values of the weight values w1, w2, and w3 can be derived.
For the weight values of the prediction parameters, in the prediction method provided by the present disclosure, it may be determined. Having obtained the weight value for the prediction parameter in the manner described above, the weight value may be applied to the sales prediction for a given month.
The present disclosure provides a method for predicting goods sales, as shown in fig. 8, fig. 8 shows a flowchart of a method for correcting weight values of prediction parameters according to an exemplary embodiment of the present disclosure:
in step S13131, the weight value of each prediction parameter is corrected using an L2 regularization penalty function.
In order to ensure the accuracy of the weight values, when the weight values of the prediction parameters are obtained, the weight values of the prediction parameters may be corrected by using a penalty function. The penalty function may be an L2 regularization penalty function.
The present disclosure provides a method for predicting sales of goods, as shown in fig. 9, a flowchart of a method for acquiring historical sales data of goods to be predicted in step S11 shown in fig. 1 is shown in fig. 9:
in step S111, historical sales data of the goods to be forecasted within a second preset period is obtained.
In the method for predicting the sales volume of the goods provided by the present disclosure, the acquisition of the historical sales data is particularly important in order to not only ensure the accuracy of the sales volume prediction of a given month, but also avoid the acquisition of excessive redundant data. The expected period of historical sales data may thus be determined, which may be 6 months or three months, for example.
When the sales data of the next month starts to be predicted after the sales volume of a certain month is predicted, the sales data that has been generated in the certain month may be taken as the historical data of predicting the next month. For example, when sales data prediction is performed for april, the history data used is history data that is advanced three months from april, that is, history data of january, february, and march. When the sales data prediction is made for month of february, the actually generated data of month of april may be taken as the history data of the predicted sales data of month of february, then the history data is that of month of february, month of march, and month of april. In this way, the accuracy of the prediction is increased.
The present disclosure provides a method for predicting sales of goods, as shown in fig. 10, and a flowchart of the method for predicting sales of goods according to an exemplary illustration of the present disclosure is shown in fig. 10:
in step S14, a sales adjustment coefficient for each day in a given month is acquired;
in step S15, the sales volume per day in a given month is determined based on the sales volume adjustment coefficient.
After the sales volume for a given month is obtained, the daily sales volume for the given month can also be predicted, so that the flexibility of prediction of the sales volume of the good can be improved. The daily sales volume is determined by obtaining an adjustment factor for the daily sales volume in a given month.
The present disclosure provides a method for predicting sales of goods, as shown in fig. 11, fig. 11 shows a flowchart of a method for obtaining a daily sales adjustment coefficient in a given month in step S14 in fig. 10:
in step S151, the category of each day in a given month is determined.
In step S152, a sales adjustment factor for each day in the given month is determined based on the determined category of each day in the given month.
In order to determine the sales adjustment coefficient for each day in a given month, the days in the given month may be classified according to a preset rule, and the sales adjustment coefficient may be determined according to the determined classification. The category of each day in a given month may be determined based on weekdays, non-weekdays, weekends, holidays, or promotional days. And in the dates of different categories, the corresponding sales volume adjustment coefficients can be determined according to the corresponding historical sales volume data. For example, if the average sales amount on weekdays in the historical sales amount data is taken as the basic data, that is, the sales amount adjustment coefficient thereof is 1, then for the sales amount adjustment coefficient on each day in the corresponding date as a promotion day in the given month, the average sales amount on the promotion day in the historical sales amount data may be divided by the average sales amount on the weekdays in the historical sales amount data to obtain a value as the sales amount adjustment coefficient on each day in the corresponding date as a promotion day in the given month.
The present disclosure provides a method for predicting sales of goods, as shown in fig. 12, and a flowchart of a method for predicting sales of goods shown in fig. 12 according to an exemplary embodiment of the present disclosure is shown.
In step S16, the sales volume adjustment coefficient for each day in a given month is updated according to a preset cycle.
In order to more flexibly predict the sales volume per day, the sales volume adjustment coefficient per day in a given month may be periodically updated according to a preset period. For example, the daily sales adjustment factor in a given month may be updated on a weekly basis. For example, after predicting the sales volume in april, after determining the daily sales volume according to the above steps, after the first week has elapsed, the sales volume adjustment coefficient for each day in a given month may be re-determined using the actually generated sales data of the first week as historical data, and then the sales volume adjustment coefficient for each day in the second week may be updated, and so on, in cycles of weeks, to update the sales volume adjustment coefficient for each day in the given month.
In one exemplary embodiment of the present disclosure, an apparatus for predicting a sales amount of an item is provided. As shown in figure 13 of the drawings, in which,
FIG. 13 is a block diagram illustrating an apparatus for forecasting sales of items, according to an example embodiment. Referring to fig. 13, the apparatus includes an acquisition module 131, a determination module 132, a prediction module 133, an optimal confidence value acquisition module 134, a weight value determination module 135, a correction module 136, a daily sales adjustment coefficient acquisition module 137, a daily sales determination module 138, and an update module 139.
An obtaining module 131 configured to obtain historical sales data of the goods to be forecasted;
a determination module 132 configured to determine a prediction parameter based on the historical sales data;
a prediction module 133 configured to obtain the predicted sales volume for the given month through a linear regression model based on the determined prediction parameters.
Wherein the prediction module 133 is configured to:
acquiring parameter values and weight values of the prediction parameters;
and obtaining the predicted sales volume of the given month through the linear regression model according to the parameter values and the weight values of the prediction parameters.
Wherein the prediction module 133 is configured to:
obtaining the product of the parameter value and the weight value of the prediction parameter;
and summing the products of the parameter values and the weight values of all the prediction parameters to obtain the predicted sales volume of the given month.
Wherein the prediction parameters include one or more of the following parameters:
the mean value of the same-ratio sales volume, the mean value of the ring-ratio sales volume, the variance of the historical sales data, the poisson distribution value of the historical sales volume data, the mean value of the historical sales volume data, and the slope value of the historical sales volume data;
wherein the slope value of the historical sales data is a value of an absolute value of a difference of sales data of a latest month minus sales data of an earliest month in the historical sales data divided by a total number of months in which the historical sales data is counted.
Wherein, when the prediction parameter comprises a poisson distribution value of the historical sales data, the determination module 132 is configured to:
acquiring a mean value and a confidence value of historical sales data in a first preset period;
and acquiring a Poisson distribution value of the historical sales data according to the acquired mean value and the confidence value of the historical sales data in the first preset period.
Wherein the prediction apparatus further comprises an optimal confidence value obtaining module 134, the optimal confidence value obtaining module 134 is configured to:
selecting T confidence values in a preset interval, wherein T is a positive integer greater than or equal to 1;
calculating a poisson distribution value corresponding to each confidence value;
and determining an optimal confidence value through a prediction model according to the calculated Poisson distribution value.
Wherein the predicting means further comprises a weight value determination module 135, the weight value determination module 135 being configured to:
determining sales data of each month in the historical sales data;
determining a prediction parameter value for each prediction parameter in each month;
and determining the weight value of each prediction parameter according to the sales data of each month and the prediction parameter value of each prediction parameter in each month.
Wherein the prediction apparatus further comprises a correction module 136 configured to correct the weight value of each prediction parameter using an L2 regularization penalty function.
Wherein the obtaining module 131 is configured to:
and acquiring historical sales data of the goods to be predicted in a second preset period.
Wherein the prediction apparatus further comprises:
a daily sales adjustment coefficient acquisition module 137 configured to acquire a daily sales adjustment coefficient in the given month;
a daily sales determination module 138 configured to determine a daily sales in the given month based on the sales adjustment coefficient.
Wherein the daily sales adjustment factor acquisition module 137 is configured to:
determining a category for each day in the given month;
determining a sales volume adjustment factor for each day in the given month according to the determined category of each day in the given month.
Wherein the predicting means further comprises an updating module 139 configured to update the sales volume adjustment coefficient for each day in the given month according to a preset cycle.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
FIG. 14 is a block diagram illustrating a forecasting apparatus 1400 for a quantity sold for an item, according to an example embodiment. For example, the apparatus 1400 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 14, apparatus 1400 may include one or more of the following components: a processing component 1402, a memory 1404, a power component 1406, a multimedia component 1408, an audio component 1410, an input/output (I/O) interface 1412, a sensor component 1414, and a communication component 1416.
The processing component 1402 generally controls the overall operation of the device 1400, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. Processing component 1402 may include one or more processors 1420 to execute instructions to perform all or a portion of the steps of the methods described above. Further, processing component 1402 can include one or more modules that facilitate interaction between processing component 1402 and other components. For example, the processing component 1402 can include a multimedia module to facilitate interaction between the multimedia component 1408 and the processing component 1402.
The memory 1404 is configured to store various types of data to support operation at the device 1400. Examples of such data include instructions for any application or method operating on device 1400, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 1404 may be implemented by any type of volatile or non-volatile storage device or combination of devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 1408 includes a screen that provides an output interface between the device 1400 and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1408 includes a front-facing camera and/or a rear-facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 1400 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 1410 is configured to output and/or input audio signals. For example, the audio component 1410 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 1400 is in operating modes, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 1404 or transmitted via the communication component 1416. In some embodiments, audio component 1410 further includes a speaker for outputting audio signals.
I/O interface 1412 provides an interface between processing component 1402 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor component 1414 includes one or more sensors for providing various aspects of state assessment for the apparatus 1400. For example, the sensor component 1414 may detect an open/closed state of the device 1400, a relative positioning of components, such as a display and keypad of the apparatus 1400, a change in position of the apparatus 1400 or a component of the apparatus 1400, the presence or absence of user contact with the apparatus 1400, an orientation or acceleration/deceleration of the apparatus 1400, and a change in temperature of the apparatus 1400. The sensor assembly 1414 may include a proximity sensor configured to detect the presence of a nearby object in the absence of any physical contact. The sensor assembly 1414 may also include a photosensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1414 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 1416 is configured to facilitate wired or wireless communication between the apparatus 1400 and other devices. The device 1400 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 1416 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1416 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 1400 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as the memory 1404 that includes instructions executable by the processor 1420 of the apparatus 1400 to perform the above-described method. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
A non-transitory computer readable storage medium having instructions therein, which when executed by a processor of a mobile terminal, enable the mobile terminal to perform a method of predicting a quantity of goods sold, the method comprising:
acquiring historical sales data of goods to be predicted;
determining a prediction parameter according to the historical sales data;
and obtaining the predicted sales volume of the given month through a linear regression model according to the determined prediction parameters.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (26)
1. A method for predicting goods sales volume is applied to electronic equipment, and is characterized by comprising the following steps:
acquiring historical sales data of goods to be predicted;
determining a prediction parameter according to the historical sales data;
and obtaining the predicted sales volume of the given month through a linear regression model according to the determined prediction parameters.
2. The method of predicting the sales of goods according to claim 1, wherein said obtaining the predicted sales for a given month by a linear regression model based on the determined prediction parameters comprises:
acquiring parameter values and weight values of the prediction parameters;
and obtaining the predicted sales volume of the given month through the linear regression model according to the parameter values and the weight values of the prediction parameters.
3. The method of predicting the sales amount of goods according to claim 2, wherein the obtaining the predicted sales amount for the given month by the linear regression model according to the parameter values and the weight values of the prediction parameters comprises:
obtaining the product of the parameter value and the weight value of the prediction parameter;
and summing the products of the parameter values and the weight values of all the prediction parameters to obtain the predicted sales volume of the given month.
4. The method of predicting the quantity sold of goods according to claim 1, wherein the prediction parameters include one or more of the following parameters:
the mean value of the same-ratio sales volume, the mean value of the ring-ratio sales volume, the variance of the historical sales data, the poisson distribution value of the historical sales volume data, the mean value of the historical sales volume data, and the slope value of the historical sales volume data;
wherein the slope value of the historical sales data is a value of an absolute value of a difference of sales data of a latest month minus sales data of an earliest month in the historical sales data divided by a total number of months in which the historical sales data is counted.
5. The method of predicting the sales amount of the good according to claim 4, wherein when the prediction parameter includes a poisson distribution value of the historical sales amount data, the poisson distribution value of the historical sales amount data is obtained as follows:
acquiring a mean value and a confidence value of historical sales data in a first preset period;
and acquiring a Poisson distribution value of the historical sales data according to the acquired mean value and the confidence value of the historical sales data in the first preset period.
6. The method of forecasting the quantity sold of the good according to claim 5, further comprising obtaining an optimal confidence value;
the obtaining the optimal confidence value comprises:
selecting T confidence values in a preset interval, wherein T is a positive integer greater than or equal to 1;
calculating a poisson distribution value corresponding to each confidence value;
and determining an optimal confidence value through a prediction model according to the calculated Poisson distribution value.
7. The method of predicting the sales of goods according to claim 2, further comprising:
determining a weight value of the prediction parameter;
the determining the weight values of the prediction parameters comprises:
determining sales data of each month in the historical sales data;
determining a prediction parameter value for each prediction parameter in each month;
and determining the weight value of each prediction parameter according to the sales data of each month and the prediction parameter value of each prediction parameter in each month.
8. The method of forecasting the sales volume of the good according to claim 7, further comprising:
the weight values for each prediction parameter are corrected using an L2 regularized penalty function.
9. The method for forecasting the sales volume of the goods according to claim 1, wherein the step of obtaining the historical sales volume data of the goods to be forecasted comprises the steps of:
and acquiring historical sales data of the goods to be predicted in a second preset period.
10. The method of forecasting the sales volume of the good according to claim 1, further comprising:
obtaining a sales volume adjustment factor for each day in the given month;
determining the sales volume per day in the given month based on the sales volume adjustment coefficient.
11. The method of predicting the sales volume of the good according to claim 10, wherein said obtaining the daily sales volume adjustment coefficient for the given month comprises:
determining a category for each day in the given month;
determining a sales volume adjustment factor for each day in the given month according to the determined category of each day in the given month.
12. The method of forecasting the sales volume of the good according to claim 11, further comprising:
updating the daily sales adjustment coefficient for the given month according to a preset period.
13. An apparatus for predicting sales of goods, comprising:
the acquisition module is configured to acquire historical sales data of goods to be predicted;
a determination module configured to determine a prediction parameter from the historical sales data;
a prediction module configured to obtain a predicted sales volume for the given month via a linear regression model based on the determined prediction parameters.
14. The item sales forecasting apparatus of claim 13, wherein the forecasting module is configured to:
acquiring parameter values and weight values of the prediction parameters;
and obtaining the predicted sales volume of the given month through the linear regression model according to the parameter values and the weight values of the prediction parameters.
15. The item sales forecasting apparatus of claim 14, wherein the forecasting module is configured to:
obtaining the product of the parameter value and the weight value of the prediction parameter;
and summing the products of the parameter values and the weight values of all the prediction parameters to obtain the predicted sales volume of the given month.
16. The device for predicting the sales of goods according to claim 13, wherein the prediction parameters include one or more of the following parameters:
the mean value of the same-ratio sales volume, the mean value of the ring-ratio sales volume, the variance of the historical sales data, the poisson distribution value of the historical sales volume data, the mean value of the historical sales volume data, and the slope value of the historical sales volume data;
wherein the slope value of the historical sales data is a value of an absolute value of a difference of sales data of a latest month minus sales data of an earliest month in the historical sales data divided by a total number of months in which the historical sales data is counted.
17. The item sales prediction apparatus of claim 16, wherein when the prediction parameter comprises a poisson distribution value of the historical sales data, the determination module is configured to:
acquiring a mean value and a confidence value of historical sales data in a first preset period;
and acquiring a Poisson distribution value of the historical sales data according to the acquired mean value and the confidence value of the historical sales data in the first preset period.
18. The device of claim 17, further comprising an optimal confidence value acquisition module configured to:
selecting T confidence values in a preset interval, wherein T is a positive integer greater than or equal to 1;
calculating a poisson distribution value corresponding to each confidence value;
and determining an optimal confidence value through a prediction model according to the calculated Poisson distribution value.
19. The item sales forecasting apparatus of claim 14, further comprising a weight value determining module configured to:
determining sales data of each month in the historical sales data;
determining a prediction parameter value for each prediction parameter in each month;
and determining the weight value of each prediction parameter according to the sales data of each month and the prediction parameter value of each prediction parameter in each month.
20. The device for predicting the sales of goods according to claim 19, further comprising:
a correction module configured to correct the weight value of each prediction parameter using an L2 regularization penalty function.
21. The item sales forecasting apparatus of claim 13, wherein the obtaining module is configured to:
and acquiring historical sales data of the goods to be predicted in a second preset period.
22. The apparatus for predicting the sales of goods according to claim 13, further comprising:
a daily sales adjustment coefficient acquisition module configured to acquire a daily sales adjustment coefficient in the given month;
a daily sales determination module configured to determine a daily sales in the given month based on the sales adjustment coefficient.
23. The item sales prediction apparatus of claim 22, wherein the daily sales adjustment factor acquisition module is configured to:
determining a category for each day in the given month;
determining a sales volume adjustment factor for each day in the given month according to the determined category of each day in the given month.
24. The device for predicting the sales of goods according to claim 23, further comprising:
an updating module configured to update the sales volume adjustment coefficient for each day in the given month according to a preset cycle.
25. An apparatus for predicting sales of goods, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring historical sales data of goods to be predicted;
determining a prediction parameter according to the historical sales data;
and obtaining the predicted sales volume of the given month through a linear regression model according to the determined prediction parameters.
26. A non-transitory computer readable storage medium having instructions therein, which when executed by a processor of a mobile terminal, enable the mobile terminal to perform a method of predicting a quantity of goods sold, the method comprising:
acquiring historical sales data of goods to be predicted;
determining a prediction parameter according to the historical sales data;
and obtaining the predicted sales volume of the given month through a linear regression model according to the determined prediction parameters.
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