CN109426891A

CN109426891A - It is a kind of high to send the forecasting system and method for turning electronic banking product

Info

Publication number: CN109426891A
Application number: CN201810552759.9A
Authority: CN
Inventors: 李燕伟; 夏耘海; 王甲樑; 段立新
Original assignee: Guoxin Youe Data Co Ltd
Current assignee: Guoxin Youe Data Co Ltd
Priority date: 2018-05-31
Filing date: 2018-05-31
Publication date: 2019-03-05

Abstract

This application provides a kind of high send to turn the forecasting system and method for electronic banking product, wherein, the system includes: data obtaining module, for obtaining the historical transactional information of every simple electric financial product, the essential attribute information and evaluation attributes information of affiliated company and corresponding history height send and turn behavioural information；Wherein, history height, which send to change one's profession, includes whether that height, which occurs, send the record turned for information；Model training module, for historical transactional information, essential attribute information and evaluation attributes information to be sent to the independent variable for turning electronic banking product prediction model as height, whether the high dependent variable for sending the record turned as model will be occurred, training height, which is sent, turns electronic banking product prediction model；Height, which is sent, turns prediction module, turns electronic banking product prediction model for sending based on trained height, and prediction target electronic financial product occurs height and send the result turned.The efficiency and accuracy rate of the application prediction are higher, and can satisfy market and send the requirement of real-time for turning electronic banking product prediction to height.

Description

It is a kind of high to send the forecasting system and method for turning electronic banking product

Technical field

This application involves electronic banking product data digging technology fields, turn electronics gold in particular to a kind of high send Melt the forecasting system and method for product.

Background technique

As the electronic banking product market of market economy important feature, number is cared for just from that day of birth with ten million The heart of investor.Now there are more and more electronic banking products (such as stock, bond, insurance) for investor's choosing in the market It selects, each listed company correspondingly provides multiple finance to investor and refer to allow investor to understand the financial situation of itself Mark, by taking stock as an example, which can be per share capital surplus, per share undistributed profit etc..For the electronics gold of magnanimity Melt product data, investor often tends to therefrom select the height with appreciation potential according to above-mentioned financial index and send to turn electronics Financial product, this is mainly due to investor it has been generally acknowledged that " height, which is sent, to be turned " delivers listed company's future performance to market and will keep The positive signal of high growth, while demand of the market to " height, which is sent, to be turned ", also can play the role of adding fuel to the flames to share price.

The relevant technologies generally use manual type and analyze above-mentioned financial index, turn electronic banking production to realize that height is sent The prediction of product, however, the workload of artificial prediction mode be it is very huge, this directly results in height and send that turn electronic banking product pre- The efficiency and accuracy rate of survey are lower, while being also unable to satisfy investor to the requirement of real-time of electronic banking product prediction.

Summary of the invention

In view of this, the application be designed to provide it is a kind of it is high send the forecasting system and method for turning electronic banking product, The prediction for turning electronic banking product is sent to height to realize, the efficiency and accuracy rate of prediction are higher.

In a first aspect, the embodiment of the present application provides and a kind of high send the forecasting system for turning electronic banking product, comprising:

Data obtaining module, for obtaining the historical transactional information of every simple electric financial product, simple electric gold Melt the essential attribute information and evaluation attributes information of the affiliated company of product, and the history corresponding to the simple electric financial product Height, which is sent, turns behavioural information；Wherein, the history height, which send to change one's profession, includes whether that height, which occurs, send the record turned for information；

Model training module is used for the historical transactional information, the essential attribute information and the evaluation attributes Information send the independent variable for turning electronic banking product prediction model as height, send the record turned as described in the height that whether occurs Height send the dependent variable for turning electronic banking product prediction model, and the training height, which is sent, turns electronic banking product prediction model；

Height, which is sent, turns prediction module, turns electronic banking product prediction model for sending based on the trained height, predicts mesh Mark electronic banking product occurs height and send the result turned.

With reference to first aspect, the embodiment of the present application provides the first possible embodiment of first aspect, wherein institute Stating essential attribute information includes one of following information or a variety of: CompanyAddress's information, company size information, the affiliated row of company Industry information, associate's educational background information；The evaluation attributes information includes one of following information or a variety of: company is in stock The number information that item number information that the frequency information of middle appearance, company occur in news, company occur in comment.

With reference to first aspect, the embodiment of the present application provides second of possible embodiment of first aspect, wherein institute Stating model training module includes:

Characteristic information determination unit should for the historical transactional information of every simple electric financial product based on acquisition The essential attribute information and evaluation attributes information of the affiliated company of simple electric financial product, determine the simple electric financial product Characteristic information；

Model training unit, for the high note sent and turned whether to occur by corresponding using the characteristic information as independent variable Record carries out at least one wheel model training as dependent variable；Wherein, each round model training is participated in by least two candidate families；

Prediction model generation unit, for by the combination of the smallest model of error rate in each round model training, as instruction The height perfected, which is sent, turns electronic banking product prediction model.

The possible embodiment of second with reference to first aspect, the embodiment of the present application provide the third of first aspect Possible embodiment, wherein the model training unit, comprising:

Model training subelement, the simple electric financial product for being used based on epicycle training determine that wheel training makes The argument value of the characteristic information of each simple electric financial product and the dependent variable value of the simple electric financial product are right At least two candidate families for participating in epicycle training are trained；

Model determines subelement, for according to epicycle training as a result, from at least two candidate moulds for participating in epicycle training The minimum candidate family of error rate is determined in type；

Error product determines subelement, for determining the error sample of candidate family result mistake in epicycle training Electronic banking product；

Weight updates subelement, for updating rule based on default weight, by the weight of simple electric financial product that malfunctions It updates, and the weight according to the error simple electric financial product updates, updates the simple electric that the epicycle training uses The weight of other simple electric financial products in financial product；

Stratified sampling handles subelement, right for the present weight according to updated every simple electric financial product More simple electric financial products for participating in epicycle training carry out stratified sampling processing, and obtaining next round training needs sample to be used This electronic banking product, into next round training, it includes that the next round training, which needs simple electric financial product to be used, Wrong simple electric financial product.

The third possible embodiment with reference to first aspect, the embodiment of the present application provide the 4th kind of first aspect Possible embodiment, wherein the model determines that subelement is specifically used for:

Based on the historical transactional information of every simple electric financial product, the base of the affiliated company of simple electric financial product This attribute information and evaluation attributes information determine the argument value of the current independent variable of the correspondence of the simple electric financial product and work as The dependent variable value of antecedents；

Successively the correspondence argument value of every simple electric financial product is inputted into epicycle respectively and completes trained at least two A candidate family, obtains whether the simple electric financial product occurs the high prediction result sent and turned；

According to the prediction result of each sample electronic banking product and the high record for sending and turning whether occurs, from this training in rotation of participation The minimum candidate family of error rate is determined at least two experienced candidate families.

The third possible embodiment or the 4th kind of possible embodiment with reference to first aspect, the embodiment of the present application Provide the 5th kind of possible embodiment of first aspect, wherein the stratified sampling processing subelement is specifically used for:

From the error simple electric financial product, determine that present weight is greater than the error sample electricity of default initial weight Sub- financial product, as first sample electronic banking product；

According to the quantity and preset quantity relationship of determining first sample electronic banking product, make from epicycle training In other simple electric financial products in simple electric financial product, it is default to determine that the present weight of corresponding number is less than Second simple electric financial product of initial weight；And

It needs to make using the first sample electronic banking product and the second simple electric financial product as next round training Simple electric financial product.

The third possible embodiment or the 4th kind of possible embodiment with reference to first aspect, the embodiment of the present application Provide the 6th kind of possible embodiment of first aspect, wherein the model training module further include:

Weight determining unit determines epicycle model training for the default value relationship according to error rate and Model Weight The weight of the minimum candidate family of middle error rate；Wherein, the default value relationship meets that error rate is smaller, and Model Weight is got over It is high；

The prediction model generation unit is specifically used for the smallest model of error rate in each round model training and its right The weighted array for answering Model Weight is sent as trained height and turns electronic banking product prediction model.

Second aspect, the embodiment of the present application also provides a kind of high send to turn the prediction technique of electronic banking product, comprising:

The historical transactional information of every simple electric financial product is obtained, the base of the affiliated company of simple electric financial product This attribute information and evaluation attributes information, and history height corresponding to the simple electric financial product send and turn behavioural information；Its In, the history height, which send to change one's profession, includes whether that height, which occurs, send the record turned for information；

The historical transactional information, the essential attribute information and the evaluation attributes information are sent as height and turns electronics Whether the independent variable of financial product prediction model occurs high to send the record turned to send as the height to turn electronic banking product using described The dependent variable of prediction model, the training height, which is sent, turns electronic banking product prediction model；

It is sent based on the trained height and turns electronic banking product prediction model, prediction target electronic financial product occurs high Send the result turned.

In conjunction with second aspect, the embodiment of the present application provides the first possible embodiment of second aspect, wherein institute Stating essential attribute information includes one of following information or a variety of: CompanyAddress's information, company size information, the affiliated row of company Industry information, associate's educational background information；The evaluation attributes information includes one of following information or a variety of: company is in stock The number information that item number information that the frequency information of middle appearance, company occur in news, company occur in comment.

In conjunction with second aspect, the embodiment of the present application provides second of possible embodiment of second aspect, wherein institute It states the training height and send and turn electronic banking product prediction model, comprising:

The historical transactional information of every simple electric financial product based on acquisition, public affairs belonging to the simple electric financial product The essential attribute information and evaluation attributes information of department, determine the characteristic information of the simple electric financial product；

Using the characteristic information as independent variable, using it is corresponding whether occur it is high send the record turned as dependent variable carry out to Few wheel model training；Wherein, each round model training is participated in by least two candidate families；

By the combination of the smallest model of error rate in each round model training, is sent as trained height and turn electronic banking production Product prediction model.

Height provided by the embodiments of the present application send the forecasting system and method for turning electronic banking product, and data obtaining module obtains The historical transactional information of every simple electric financial product is taken, the essential attribute information of the affiliated company of simple electric financial product It send with evaluation attributes information, and history height corresponding to the simple electric financial product and turns behavioural information；Wherein, the history Height, which send to change one's profession, includes whether that height, which occurs, send the record turned for information；Model training module is by the historical transactional information, the base This attribute information and the evaluation attributes information send the independent variable for turning electronic banking product prediction model as height, are by described The no height that occurs send the record turned to send the dependent variable for turning electronic banking product prediction model as the height, and the training height, which is sent, turns electricity Interest melts product prediction model；Height, which is sent, to be turned prediction module and is based on the trained height to send and turn electronic banking product prediction model, Prediction target electronic financial product occurs height and send turning as a result, that is, it is sent by the height of training turns electronic banking product prediction Model realization, which send the height of target electronic financial product, turns prediction, and the efficiency and accuracy rate of prediction are higher, and can satisfy city The requirement of real-time for turning electronic banking product prediction is sent to height in field.

To enable the above objects, features, and advantages of the application to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate Appended attached drawing, is described in detail below.

Detailed description of the invention

Technical solution in ord to more clearly illustrate embodiments of the present application, below will be to needed in the embodiment attached Figure is briefly described, it should be understood that the following drawings illustrates only some embodiments of the application, therefore is not construed as pair The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this A little attached drawings obtain other relevant attached drawings.

Fig. 1 shows a kind of high structure for sending the forecasting system for turning electronic banking product provided by the embodiment of the present application and shows It is intended to；

High model is sent in the forecasting system for turning electronic banking product to instruct Fig. 2 shows a kind of provided by the embodiment of the present application Practice the structural schematic diagram of module；

Fig. 3, which is shown, a kind of provided by the embodiment of the present application high send in the forecasting system for turning electronic banking product model to instruct Practice the structural schematic diagram of unit；

Fig. 4, which is shown, a kind of provided by the embodiment of the present application high send the process for turning the prediction technique of electronic banking product Figure；

Fig. 5 shows another kind height provided by the embodiment of the present application and send the process for turning the prediction technique of electronic banking product Figure；

Fig. 6 shows another kind height provided by the embodiment of the present application and send the process for turning the prediction technique of electronic banking product Figure；

Fig. 7 shows another kind height provided by the embodiment of the present application and send the process for turning the prediction technique of electronic banking product Figure；

Fig. 8 shows another kind height provided by the embodiment of the present application and send the process for turning the prediction technique of electronic banking product Figure；

Fig. 9 shows another kind height provided by the embodiment of the present application and send the process for turning the prediction technique of electronic banking product Figure；

Figure 10 shows a kind of structural schematic diagram of computer equipment provided by the embodiment of the present application.

Main element symbol description:

11, data obtaining module；22, model training module；33, height, which is sent, turns prediction module；221, characteristic information determines single Member；222, model training unit；223, prediction model generation unit；224, weight determining unit；2221, model training is single Member；2222, model determines subelement；2223, error product determines subelement；2224, weight updates subelement；2225, it is layered Sample process subelement；1000, processor；2000, memory；3000, bus.

Specific embodiment

To keep the purposes, technical schemes and advantages of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application Middle attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only It is some embodiments of the present application, instead of all the embodiments.The application being usually described and illustrated herein in the accompanying drawings is real The component for applying example can be arranged and be designed with a variety of different configurations.Therefore, below to the application's provided in the accompanying drawings The detailed description of embodiment is not intended to limit claimed scope of the present application, but is merely representative of the selected reality of the application Apply example.Based on embodiments herein, those skilled in the art institute obtained without making creative work There are other embodiments, shall fall in the protection scope of this application.

In view of using manual type to carry out the workload right and wrong that height send the prediction for turning electronic banking product in the related technology Often huge, this directly results in height and send the efficiency for turning electronic banking product prediction and accuracy rate lower, while being also unable to satisfy Requirement of real-time of the investor to electronic banking product prediction.In view of this, the embodiment of the present application provide it is a kind of it is high send turn electricity The forecasting system and method for sub- financial product turn the prediction of electronic banking product to realize to send height, the efficiency of prediction and accurate Rate is higher.

As shown in Figure 1, sending the structural representation for turning the forecasting system of electronic banking product for height provided by the embodiments of the present application Figure, the forecasting system include:

Data obtaining module 11, for obtaining the historical transactional information of every simple electric financial product, the simple electric The essential attribute information and evaluation attributes information of the affiliated company of financial product, and going through corresponding to the simple electric financial product History height, which is sent, turns behavioural information；Wherein, history height, which send to change one's profession, includes whether that height, which occurs, send the record turned for information；

Model training module 22, for using historical transactional information, essential attribute information and evaluation attributes information as height The independent variable for turning electronic banking product prediction model is sent, whether will occur high to send the record turned to send as height to turn electronic banking product The dependent variable of prediction model, training height, which is sent, turns electronic banking product prediction model；

Height, which is sent, turns prediction module 33, turns electronic banking product prediction model for sending based on trained height, predicts target Electronic banking product occurs height and send the result turned.

Specifically, height provided by the embodiments of the present application send the forecasting system for turning electronic banking product, pass through acquisition of information mould Block 11 obtains the essential attribute information of historical transactional information and its affiliated company in relation to simple electric financial product and evaluation belongs to Property information, and include whether that height, which occurs, send the history height of the record turned to send and turn behavioural information, and above- mentioned information are obtained into module 11 module transfers obtained are to model training module 22, in order to which the model training module 22 can be according to the training of the information of acquisition Height, which is sent, turns electronic banking product prediction model, and trained height sent and turns supreme send of electronic banking product prediction model transmission and turns Prediction module 33 send the result turned to predict, it is seen then that it is sent by the height of training so that height occurs to target electronic financial product Turn electronic banking product prediction model realization and send a turn prediction to the height of target electronic financial product, the efficiency and accuracy rate of prediction are equal It is higher.In addition, in the embodiment of the present application, above-mentioned height send turn electronic banking product prediction model can be it is trained in advance, this Sample can need the arbitrary target electronic banking product predicted progress height to send a turn prediction to investor, send to meet market to height Turn the requirement of real-time of electronic banking product prediction.

Wherein, above-mentioned electronic banking product can be stock, can also be bond, can also be insurance etc., next with Stock carries out specific example as electronic banking product.The application obtains the historical transactional information of more sample stocks and right The history height answered, which is sent, turns behavioural information.Above-mentioned historical transactional information and history height, which are sent, to be turned behavioural information and can be from internet net The data-interface that (such as great wisdom, large stock trading website) accurately opens of standing is obtained, and be can also be and is climbed using network The historical transactional information of desired acquisition and history height are sent and are changed one's profession as information crawler to local computer equipment by worm technology.

For historical transactional information, debit card, credit card etc. that the embodiment of the present application can be held by obtaining user The pertinent transaction information of generation is determined, the relationship trading that can also be generated on the network trading platform of binding by user Information is determined.Wherein, above-mentioned historical transactional information includes but is not limited to: per share capital surplus, per share undistributed profit, benefit Profit increases by a year-on-year basis, stock price, the stock's coming-into market date, closing price, total market capitalisation, shareholding equity, share price rise number statistics, under stock Fall the letter such as number statistics, advance versus decline width, net assets income ratio, basic earnings per share, net assets per share, mechanism shareholding ratio Breath.For history height send and turns behavioural information, it can be obtained by inputting personal share code to stock public address system.

The embodiment of the present application not only obtains the historical transactional information of sample stock and corresponding history height send change one's profession for Information also obtains the essential attribute information and evaluation attributes information of the above-mentioned affiliated company of sample stock, wherein above-mentioned company Essential attribute information can be obtained by inputting in corresponding business information of enterprise inquiry net to inquire enterprise name, and this belongs to substantially Property information include but is not limited to: CompanyAddress's information, company size information, the affiliated trade information of company, associate's educational background letter Breath；Above-mentioned evaluation attributes information send the acquisition modes for turning behavioural information similar with above-mentioned historical transactional information and history height, I.e., it is possible to obtained by data-interface and data crawler mode, unlike, which is mutual from other Networking website, such as today's tops, Sina weibo acquisition, details are not described herein.Wherein, which includes but not Be limited to: company's item number information that the frequency information of appearance, company occur in news in stock, company occur in comment Number information.

It is worth noting that the embodiment of the present application can select selection in different time periods to go through according to different forecast demands History Transaction Information, evaluation attributes information and history height, which are sent, turns behavioural information, and the embodiment of the present application not only can be using in 1 year The information of first three quarters come predict whether to occur in the 4th season height send turn, can also be predicted not using the information of the previous year Whether to occur for 1 year height and send to turn, at this moment, corresponding information can be chosen for different forecast demand.

It is sent in height and turns electronic banking product prediction model training stage, with the historical transactional information of above-mentioned acquisition, basic category Property information and evaluation attributes information send the independent variable for turning electronic banking product prediction model as height to be trained, with corresponding Height, which whether occurs, is sent the record turned as dependent variable, and training obtains height and send the parameter information for turning electronic banking product prediction model Deng, namely obtain trained height and give to turn electronic banking product prediction model.The embodiment of the present application can be made using disaggregated model It is sent for height and turns electronic banking product prediction model, some unknown parameter letters in model training stage i.e. train classification models The process of breath etc..Later, so that it may sent based on above-mentioned height turn electronic banking product prediction model be target electronic financial product mention It is serviced for prediction, only needs to produce the historical transactional information of target electronic financial product and the target electronic finance at this time The essential attribute information and evaluation attributes information input of the affiliated company of product are sent to trained height turns electronic banking product prediction mould In type.

In specific implementation, the embodiment of the present application can be sent based on AdaBoost classification algorithm training height turns electronic banking production Product prediction model, that is, the embodiment of the present application is the Weak Classifier different for the training of the same training set, it is then that these are weak Classifier gathers, and constitutes a stronger final classification device, and by continuous repetitive exercise, it is stronger to construct classification capacity Height, which is sent, turns electronic banking product prediction model.

Height provided by the embodiments of the present application send the forecasting system for turning electronic banking product, and data obtaining module 11 obtains often The historical transactional information of branch simple electric financial product, the essential attribute information of the affiliated company of simple electric financial product and is commented Valence attribute information, and history height corresponding to the simple electric financial product send and turn behavioural information；Wherein, history height, which is sent, changes one's profession Include whether that height, which occurs, send the record turned for information；Model training module 22 by historical transactional information, essential attribute information and Evaluation attributes information is sent as height turns the independent variable of electronic banking product prediction model, using whether occur it is high send the record that turns as Height send the dependent variable for turning electronic banking product prediction model, and training height, which is sent, turns electronic banking product prediction model；Height, which is sent, turns prediction Module 33 is based on trained height and send and turns electronic banking product prediction model, and height occurs for prediction target electronic financial product send to turn As a result, that is, it is sent by the height of training turns electronic banking product prediction model realization and send to the height of target electronic financial product Turn prediction, the efficiency and accuracy rate of prediction are higher, and can satisfy market and send to height and turn the real-time of electronic banking product prediction Property require.

Height provided by the embodiments of the present application send the forecasting system for turning electronic banking product that can also pass through data processing module The historical transactional information of above-mentioned acquisition, history height are sent and turn behavioural information and evaluation attributes information is filtered, type turns The processing such as change, derive, with the information that obtains that treated.Wherein, above-mentioned filtration treatment is referred to the missing letter in above- mentioned information Breath, duplicate message etc. are filtered operation, and the above-mentioned type conversion, which can be, to be normalized, the data of separate sources It is unified under a referential, just significant by comparison in this way, above-mentioned derivation process refers to for statistical analysis obtain Additional information can be by spreading out to Transaction Information such as including the historical transactional information of exchange hour, transaction count It is raw, obtain the associated statistical informations such as the average transaction count of certain stock.

The smallest mould of error rate that the embodiment of the present application is obtained at least one wheel model training by model training module 22 The group of type, which is combined into height and send, turns electronic banking product prediction model, and referring to fig. 2, above-mentioned model training module 22 specifically includes:

Characteristic information determination unit 221, for the historical transactional information of every simple electric financial product based on acquisition, The essential attribute information and evaluation attributes information of the affiliated company of simple electric financial product, determine the simple electric financial product Characteristic information；

Model training unit 222, for the high record for sending and turning whether to occur by corresponding using characteristic information as independent variable At least one wheel model training is carried out as dependent variable；Wherein, each round model training is participated in by least two candidate families；

Prediction model generation unit 223, for by the combination of the smallest model of error rate in each round model training, as Trained height, which is sent, turns electronic banking product prediction model.

Specifically, the embodiment of the present application passes through characteristic information determination unit 221 first is based on every simple electric finance production The historical transactional information of product, the essential attribute information and evaluation attributes information of the affiliated company of simple electric financial product determine The characteristic information of the simple electric financial product, then by model training module 22 respectively using characteristic information as independent variable, The record turned is sent to carry out at least one wheel model training as dependent variable the corresponding height that whether occurs, it is raw finally by prediction model At unit 223 by the combination of the smallest model of error rate in each round model training, is sent as trained height and turn electronic banking Product prediction model.

Wherein, the characteristic information of above-mentioned simple electric financial product refers to that (such as historical transactional information is gone through to above- mentioned information History height, which is sent, turns behavioural information and evaluation attributes information) treated as a result, features described above information can be above- mentioned information for progress Normalization as a result, can also be feature derivative as a result, the embodiment of the present application can be adaptive based on the above- mentioned information actually obtained The carry out Feature Selection answered.

In the embodiment of the present application for each round model training, can at least there are two candidate family participate in, In, above-mentioned candidate family can be neural network model, SVM (Support Vector Machine, support vector machines) model, Any combination in logistic (recurrence) model, can also be any combination of other disaggregated models.

Further, the historical transactional information of acquisition, evaluation attributes information can be more simple electric financial products The historical trading and evaluation information of first historical time section, corresponding history height, which is sent, to be turned behavioural information and can be more simple electrics The historical behavior information of second historical time section of financial product, and the first historical time section is earlier than the second historical time section；This Application embodiment in training height send turn electronic banking product prediction model complete training in actual use, can be according to electronics The historical trading and evaluation information of financial product send the height of the electronic banking product in future time and change one's profession to predict, Therefore, when being trained, the characteristic value as characteristic information can come from historical trading and the evaluation of the first historical time section Information, the historical behavior information for sending the characteristic value changed one's profession and be characterized to can come from the second historical time section as height.First history Time interval between period and the second historical time section is first time interval, and height, which is sent, turns electronic banking product prediction model Period where the historical trading and evaluation information that use when in use and needs predict whether that height, which occurs, send the future time turned Time interval between section is the second time interval, preferably, first time interval is consistent with the second time interval.

It is worth noting that the embodiment of the present application can determine that height is sent in the following way turns electronic banking product prediction mould Whether type reaches convergence.First way, the embodiment of the present application can send the instruction for turning electronic banking product prediction model using height Practice the judgment mode whether wheel number reaches default exercise wheel number (such as 3 wheel), if exercise wheel number reaches preset threshold, it is determined that on Stating height send the output for turning electronic banking product prediction model to have reached convergence, if frequency of training is not up to preset threshold, it is determined that Not up to restrain.The second way, the embodiment of the present application can also send the output for turning electronic banking product prediction model using height As a result and whether occur the judgement whether output error (such as error 0.0001) between the high record for sending and turning is less than default error Mode, if output error is less than default error, it is determined that above-mentioned height send the output for turning electronic banking product prediction model to reach To convergence, if output error is less than or equal to default error, it is determined that not up to restrain.Either above-mentioned any judgement side The combinations of the smallest model of error rate in all wheel model trainings are sent as above-mentioned height after determination reaches convergence and turn electricity by formula Interest melts product prediction model.

In the embodiment of the present application, as shown in figure 3, above-mentioned model training unit 222 specifically includes:

Model training subelement 2221, the simple electric financial product for being used based on epicycle training, determines the training in rotation Practice the argument value of the characteristic information of each simple electric financial product used and the dependent variable of the simple electric financial product Value is trained at least two candidate families for participating in epicycle training；

Model determines subelement 2222, for according to epicycle training as a result, from participate in epicycle training at least two times The minimum candidate family of error rate is determined in modeling type；

Error product determines subelement 2223, for determining the error sample of candidate family result mistake in epicycle training Electronic banking product；

Weight updates subelement 2224, for updating rule based on default weight, by the simple electric financial product that malfunctions Weight updates, and the weight according to error simple electric financial product updates, and updates the simple electric finance that epicycle training uses The weight of other simple electric financial products in product；

Stratified sampling handles subelement 2225, for the current power according to updated every simple electric financial product Weight carries out stratified sampling processing to more simple electric financial products for participating in epicycle training, obtains next round training and needs to make Simple electric financial product, trained into next round, next round training need simple electric financial product to be used includes Malfunction simple electric financial product.

Specifically, each round training uses the feature of each simple electric financial product to believe in the embodiment of the present application The dependent variable value of the argument value of breath and the simple electric financial product, at least two candidate moulds of wheel training every to above-mentioned participation Type is trained, and based on the prediction result to whole simple electric financial products, is determined from above-mentioned at least two candidate family The minimum candidate family of error rate weighs the error simple electric financial product for the training result mistake that candidate family determines It updates again.

Wherein, in order to highlight error simple electric financial product, the default weight in the embodiment of the present application updates rule and refers to Be that will the malfunction weight of simple electric financial product is turned up, the corresponding weight by correct simple electric financial product turns down.

In the specific implementation process, high in order to balance to send the predictablity rate and effect for turning electronic banking product prediction model Rate, in the embodiment of the present application, the candidate family that every wheel training uses can be the same or different, for identical in the case of no longer It repeats, it is special in the case of different for example: if last round of middle error rate is more than the candidate family of threshold value, next round can be with It does not use.

Wherein, above-mentioned model determines that subelement 2222 is specifically used for: the history based on every simple electric financial product is handed over Easy information, the essential attribute information and evaluation attributes information of the affiliated company of simple electric financial product, determines the simple electric The argument value of the current independent variable of the correspondence of financial product and dependent variable value when antecedents；Successively by every simple electric gold The correspondence argument value for melting product inputs at least two candidate families that epicycle completes training respectively, obtains simple electric finance Whether product occurs the high prediction result sent and turned；According to the prediction result of each sample electronic banking product and height whether occurs send The record turned determines the minimum candidate family of error rate from least two candidate families for participating in epicycle training.

Here, the correspondence argument value of every determining simple electric financial product is inputted this by the embodiment of the present application respectively Wheel completes at least two candidate families of training, obtains whether the simple electric financial product occurs the high prediction result sent and turned, According to the prediction result of each sample electronic banking product and the high record for sending and turning whether occurs, is trained at least from epicycle is participated in The minimum candidate family of error rate is determined in two candidate families.

It is worth noting that the embodiment of the present application is after determining the minimum candidate family of error rate, it will be according to default mistake Rate threshold value judges lowest error rate, only in lowest error rate not up to default error rate threshold, just enters next training in rotation Practice, if lowest error rate reaches default error rate threshold (such as 0.5), training terminates to epicycle, does not continue to.

In addition, the embodiment of the present application is before entering next round training, further includes: need to make to determining next round training Training data carries out class imbalance processing.The embodiment of the present application can carry out uneven processing using top sampling method, Uneven processing can also be carried out using Downsapling method, can also use SMOTE (Synthetic Minority Over- Sampling Technique) the uneven processing of method progress.Wherein, above-mentioned top sampling method refers to the too low sample of comparative example This (that is, error simple electric financial product) duplicate sampling, so that the feature of this kind of sample is arrived by model learning；It is adopted under above-mentioned Quadrat method refers to that the excessively high sample of comparative example (that is, correct simple electric financial product) reduces frequency in sampling, to prevent mould The feature of this kind of pattern sheet of type overlearning；Above-mentioned SMOTE method is referred to minority class electronic banking product (that is, error Simple electric financial product) analyze and be added in training data according to the artificial synthesized new samples of minority class sample, thus Avoid overfitting problem.In view of the good characteristic of SMOTE method, the embodiment of the present application can carry out not according to SMOTE method Balance Treatment.

In addition, above-mentioned stratified sampling processing subelement 2225 is specifically used for:

From error simple electric financial product, determine that present weight is greater than the error simple electric gold of default initial weight Melt product, as first sample electronic banking product；

According to the quantity and preset quantity relationship of determining first sample electronic banking product, used from epicycle training In other simple electric financial products in simple electric financial product, it is default initial to determine that the present weight of corresponding number is less than Second simple electric financial product of weight；And

It is needed using first sample electronic banking product and the second simple electric financial product as next round training to be used Simple electric financial product.

It here, first can be from all error samples after carrying out weight update to error simple electric financial product Determine that present weight is greater than the error simple electric finance production of some or all of default initial weight of member in electronic banking product Product, as first sample electronic banking product, which corresponds to error simple electric financial product, Then according to the quantity of above-mentioned error simple electric financial product and error simple electric financial product and correct simple electric Preset quantity relationship between financial product is (as the number of error simple electric financial product is produced equal to correct simple electric finance The number of product), determine that the present weight of corresponding number is less than the second simple electric financial product of initial weight, second sample Electronic banking product corresponds to correct simple electric financial product, by first sample electronic banking product and the second simple electric gold Melting product as next round training needs simple electric financial product to be used.

Similarly, determine that the next round training of the next round needs simple electric gold to be used according to the prediction result of next round Melt product and the above-mentioned prediction result according to the first round determines that next round training needs the tool of simple electric financial product to be used Body implementation method is similar, and so on, this will not be repeated here.

Height in the embodiment of the present application send turn electronic banking product prediction model be to rely in each round model training it is wrong The smallest model of rate is missed, as shown in Fig. 2, above-mentioned model training module 22 further include:

Weight determining unit 224 determines that epicycle model is instructed for the default value relationship according to error rate and Model Weight The weight of the minimum candidate family of error rate in white silk；Wherein, default value relationship meets that error rate is smaller, and Model Weight is higher.

Here, default value relationship can be determined according to following formula:

Wherein, α_mIndicate the determining the smallest candidate family of error rate of m wheel training Model Weight, e_mIndicate the determining lowest error rate of m wheel training, β indicates that Model Weight coefficient, the Model Weight coefficient take Being worth range is 0~1, preferably, 1/2 can be taken.

In addition, the prediction model generation unit 223 in the embodiment of the present application can also be by mistake in each round model training The weighted array of the smallest model of rate and the Model Weight determined based on above-mentioned formula, is sent as trained height and turns electronic banking Product prediction model.

Height in the embodiment of the present application, which is sent, to be turned prediction module 33 and send based on the obtained height of training to turn electronic banking product prediction Model can determine that height, which occurs, send turning as a result, the height send and turns prediction module 33 and be specifically used for, and obtain for target electronic financial product Take the historical transactional information of target electronic financial product and the essential attribute information of the affiliated company of target electronic financial product With evaluation attributes information；By the historical transactional information of target electronic financial product, essential attribute information and evaluation attributes information Trained height is inputted respectively and is sent and turns the smallest model of error rate that each training in rotation in electronic banking product prediction model is got, and is obtained To the corresponding prediction result of each error rate least model；Based on the corresponding Model Weight of each error rate least model, to each Prediction result is weighted summation, and obtain and value is determined as target electronic financial product, the high result sent and turned occurs.It can See, using preparatory trained height to send, to turn electronic banking product prediction model can rapidly and efficiently be target electronic financial product It carries out height and send a turn prediction, and the precision predicted is higher, the degree of automation is also higher.

Based on the same inventive concept, it is additionally provided in the embodiment of the present application and send the forecasting system for turning electronic banking product with high Corresponding prediction technique, the principle solved the problems, such as due to the method in the embodiment of the present application and the above-mentioned prediction system of the embodiment of the present application It unites similar, because the implementation of the method may refer to the implementation of system, overlaps will not be repeated.As shown in figure 4, real for the application It applies height provided by example and send the flow chart for turning the prediction technique of electronic banking product, this method comprises:

S101, the historical transactional information for obtaining every simple electric financial product, public affairs belonging to the simple electric financial product The essential attribute information and evaluation attributes information of department, and history height corresponding to the simple electric financial product send and change one's profession as letter Breath；Wherein, history height, which send to change one's profession, includes whether that height, which occurs, send the record turned for information；

S102, sent using historical transactional information, essential attribute information and evaluation attributes information as height turn electronic banking production The independent variable of product prediction model, by whether occur it is high send the record turned as height and send turn electronic banking product prediction model because of change Amount, training height, which is sent, turns electronic banking product prediction model；

S103, it is sent based on trained height and turns electronic banking product prediction model, prediction target electronic financial product occurs Height send the result turned.

In specific implementation, above-mentioned essential attribute information includes one of following information or a variety of: CompanyAddress's information, Company size information, the affiliated trade information of company, associate's educational background information；Evaluation attributes information includes one in following information Kind is a variety of: company's item number information that the frequency information of appearance, company occur in news in stock, company go out in comment Existing number information.

In one embodiment, as shown in figure 5, above-mentioned training height send and turns electronic banking product prediction model, comprising:

The historical transactional information of S201, every simple electric financial product based on acquisition, the simple electric financial product The essential attribute information and evaluation attributes information of affiliated company, determine the characteristic information of the simple electric financial product；

S202, using characteristic information as independent variable, whether occur high to send the record turned as dependent variable progress using corresponding At least one wheel model training；Wherein, each round model training is participated in by least two candidate families；

S203, by the combination of the smallest model of error rate in each round model training, sent as trained height and turn electronics Financial product prediction model.

In another embodiment, as shown in fig. 6, the training of every wheel performs the following operations:

S301, the simple electric financial product used based on epicycle training determine each sample electricity that wheel training uses The dependent variable value of the argument value of the characteristic information of sub- financial product and the simple electric financial product, to participation epicycle training At least two candidate families are trained；

S302, according to epicycle training as a result, from participate in epicycle training at least two candidate families in determine error rate Minimum candidate family；

S303, the error simple electric financial product for determining candidate family result mistake in epicycle training；

S304, rule is updated based on default weight, the weight for the simple electric financial product that malfunctions is updated, and according to error The weight of simple electric financial product updates, and updates other simple electrics in the simple electric financial product that epicycle training uses The weight of financial product；

S305, according to the present weight of updated every simple electric financial product, to participating in more of epicycle training Simple electric financial product carries out stratified sampling processing, and obtaining next round training needs simple electric financial product to be used, into Enter next round training, it includes error simple electric financial product that next round training, which needs simple electric financial product to be used,.

In yet another embodiment, as shown in fig. 7, according to epicycle training as a result, from epicycle training is participated at least The minimum candidate family of error rate is determined in two candidate families, comprising:

S401, the historical transactional information based on every simple electric financial product, public affairs belonging to the simple electric financial product The essential attribute information and evaluation attributes information of department, determine the independent variable of the current independent variable of the correspondence of the simple electric financial product Value and when antecedents dependent variable value；

S402, successively by the correspondence argument value of every simple electric financial product input respectively epicycle complete training extremely Few two candidate families, obtain whether the simple electric financial product occurs the high prediction result sent and turned；

S403, according to the prediction result of each sample electronic banking product and the high record for sending and turning whether occurs, from participation The minimum candidate family of error rate is determined at least two candidate families of epicycle training.

In specific implementation, after determining the minimum candidate family of error rate, further includes:

According to the default value relationship of error rate and Model Weight, the candidate that error rate is minimum in epicycle model training is determined The weight of model；Wherein, default value relationship meets that error rate is smaller, and Model Weight is higher；

By the combination of the smallest model of error rate in each round model training, is sent as trained height and turn electronic banking production Product prediction model, specifically includes:

By the weighted array of the smallest model of error rate in each round model training and its corresponding Model Weight, as training Good height, which is sent, turns electronic banking product prediction model.

In another embodiment, as shown in figure 8, according to the current power of updated every simple electric financial product Weight carries out stratified sampling processing to more simple electric financial products for participating in epicycle training, obtains next round training and needs to make Simple electric financial product, comprising:

S501, from error simple electric financial product, determine that present weight is greater than the error sample of default initial weight Electronic banking product, as first sample electronic banking product；

S502, according to the quantity and preset quantity relationship of determining first sample electronic banking product, from epicycle training In other simple electric financial products in the simple electric financial product used, it is pre- to determine that the present weight of corresponding number is less than If the second simple electric financial product of initial weight；

S503, it needs to make using first sample electronic banking product and the second simple electric financial product as next round training Simple electric financial product.

In another embodiment, turn electronic banking product prediction model as shown in figure 9, sending based on trained height, Prediction target electronic financial product occurs height and send the result turned, comprising:

Public affairs belonging to S601, the historical transactional information for obtaining target electronic financial product and the target electronic financial product The essential attribute information and evaluation attributes information of department；

S602, the historical transactional information of target electronic financial product, essential attribute information and evaluation attributes information are divided Trained height is not inputted and is sent and turns the smallest model of error rate that each training in rotation in electronic banking product prediction model is got, and is obtained The corresponding prediction result of each error rate least model；

S603, it is based on the corresponding Model Weight of each error rate least model, summation is weighted to each prediction result, and will Obtain and value is determined as target electronic financial product and the high result sent and turned occurs.

It as shown in Figure 10, is a kind of structural schematic diagram of computer equipment provided by the embodiment of the present application, the computer Equipment includes: processor 1000, memory 2000 and bus 3000, and the storage of memory 2000 executes instruction, when the device is running, Communicated between processor 1000 and memory 2000 by bus 3000, processor 1000 execute stored in memory 2000 as Under execute instruction:

The historical transactional information of every simple electric financial product is obtained, the base of the affiliated company of simple electric financial product This attribute information and evaluation attributes information, and history height corresponding to the simple electric financial product send and turn behavioural information；Its In, history height, which send to change one's profession, includes whether that height, which occurs, send the record turned for information；

Historical transactional information, essential attribute information and evaluation attributes information are sent as height and turn electronic banking product prediction Whether the independent variable of model will occur high to send the record turned to send as height to turn the dependent variable of electronic banking product prediction model, instruct White silk height, which is sent, turns electronic banking product prediction model；

It is sent based on trained height and turns electronic banking product prediction model, prediction target electronic financial product generation height, which is sent, to be turned Result.

In one embodiment, in the step of above-mentioned processor 1000 executes, above-mentioned training height, which is sent, turns electronic banking production Product prediction model, comprising:

Using characteristic information as independent variable, the record turned is sent to carry out at least one as dependent variable the corresponding height that whether occurs Take turns model training；Wherein, each round model training is participated in by least two candidate families；

In another embodiment, in the step of above-mentioned processor 1000 executes, every wheel training is performed the following operations:

Based on the simple electric financial product that epicycle training uses, each simple electric finance that wheel training uses is determined The dependent variable value of the argument value of the characteristic information of product and the simple electric financial product, at least the two of participation epicycle training A candidate family is trained；

According to epicycle training as a result, determining that error rate is minimum from least two candidate families for participating in epicycle training Candidate family；

Determine the error simple electric financial product of candidate family result mistake in epicycle training；

Rule is updated based on default weight, the weight for the simple electric financial product that malfunctions is updated, and according to error sample The weight of electronic banking product updates, and updates other simple electrics finance in the simple electric financial product that epicycle training uses The weight of product；

According to the present weight of updated every simple electric financial product, to more sample electricity for participating in epicycle training Sub- financial product carries out stratified sampling processing, and obtaining next round training needs simple electric financial product to be used, and entrance is next Wheel training, it includes error simple electric financial product that next round training, which needs simple electric financial product to be used,.

In yet another embodiment, in the step of above-mentioned processor 1000 executes, according to epicycle training as a result, from ginseng The minimum candidate family of error rate is determined at least two candidate families of epicycle training, comprising:

In specific implementation, in the step of above-mentioned processor 1000 executes, the minimum candidate family of error rate is being determined Later, further includes:

In the step of above-mentioned processor 1000 executes, by the combination of the smallest model of error rate in each round model training, It is sent as trained height and turns electronic banking product prediction model, specifically included:

In another embodiment, above-mentioned processor 1000 execute the step of in, according to updated every sample electricity The present weight of sub- financial product carries out stratified sampling processing to more simple electric financial products for participating in epicycle training, obtains Simple electric financial product to be used is needed to next round training, comprising:

In another embodiment, above-mentioned processor 1000 execute the step of in, sent based on trained height and turn electronics Financial product prediction model, prediction target electronic financial product occur height and send the result turned, comprising:

Obtain the historical transactional information of target electronic financial product and the base of the affiliated company of target electronic financial product This attribute information and evaluation attributes information；

The historical transactional information of target electronic financial product, essential attribute information and evaluation attributes information are inputted respectively Trained height, which is sent, turns the smallest model of error rate that each training in rotation in electronic banking product prediction model is got, and obtains each mistake The corresponding prediction result of rate least model；

Based on the corresponding Model Weight of each error rate least model, summation is weighted to each prediction result, and will obtain And value be determined as target electronic financial product and the result that height is sent turn occur.

The embodiment of the present application also provides a kind of computer readable storage medium, stored on the computer readable storage medium There is computer program, which executes above-mentioned height and send the prediction side for turning electronic banking product when being run by processor 1000 The step of method.

Specifically, which can be general storage medium, such as mobile disk, hard disk, on the storage medium Computer program when being run, be able to carry out above-mentioned height and send the prediction technique for turning electronic banking product, to solve current people Efficiency brought by work prediction mode and the lower problem of accuracy, and then reach to give height and turn the pre- of electronic banking product It surveys, the efficiency of prediction and the higher effect of accuracy.

Height provided by the embodiment of the present application send the computer program product for turning the prediction technique of electronic banking product, including The computer readable storage medium of program code is stored, the instruction that program code includes can be used for executing previous methods embodiment In method, specific implementation can be found in embodiment of the method, details are not described herein.

It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description It with the specific work process of device, can refer to corresponding processes in the foregoing method embodiment, details are not described herein.

If function is realized in the form of SFU software functional unit and when sold or used as an independent product, can store In a computer readable storage medium.Based on this understanding, the technical solution of the application is substantially in other words to existing Having the part for the part or the technical solution that technology contributes can be embodied in the form of software products, the computer Software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be personal meter Calculation machine, server or network equipment etc.) execute each embodiment method of the application all or part of the steps.And it is above-mentioned Storage medium includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory The various media that can store program code such as (RAM, Random Access Memory), magnetic or disk.

More than, the only specific embodiment of the application, but the protection scope of the application is not limited thereto, and it is any to be familiar with Those skilled in the art within the technical scope of the present application, can easily think of the change or the replacement, and should all cover Within the protection scope of the application.Therefore, the protection scope of the application should be subject to the protection scope in claims.

Claims

1. high the forecasting system for turning electronic banking product is sent a kind of characterized by comprising

Data obtaining module, for obtaining the historical transactional information of every simple electric financial product, which is produced The essential attribute information and evaluation attributes information of the affiliated company of product, and history height corresponding to the simple electric financial product are sent Turn behavioural information；Wherein, the history height, which send to change one's profession, includes whether that height, which occurs, send the record turned for information；

Model training module is used for the historical transactional information, the essential attribute information and the evaluation attributes information The independent variable for turning electronic banking product prediction model is sent as height, send the record turned to send as the height height that whether occurs Turn the dependent variable of electronic banking product prediction model, the training height, which is sent, turns electronic banking product prediction model；

Height, which is sent, turns prediction module, turns electronic banking product prediction model for sending based on the trained height, prediction target electricity Sub- financial product occurs height and send the result turned.

2. system according to claim 1, which is characterized in that the essential attribute information includes one of following information It is or a variety of: CompanyAddress's information, company size information, the affiliated trade information of company, associate's educational background information；The evaluation belongs to Property information include one of following information or a variety of: frequency information that company occurs in the stock, company occur in news Item number information, the number information that occurs in comment of company.

3. system according to claim 1, which is characterized in that the model training module includes:

Characteristic information determination unit, for the historical transactional information of every simple electric financial product based on acquisition, the sample The essential attribute information and evaluation attributes information of the affiliated company of electronic banking product, determine the feature of the simple electric financial product Information；

Model training unit, for the high record work for sending and turning whether to occur by corresponding using the characteristic information as independent variable At least one wheel model training is carried out for dependent variable；Wherein, each round model training is participated in by least two candidate families；

Prediction model generation unit, for by the combination of the smallest model of error rate in each round model training, as training Height send and turn electronic banking product prediction model.

4. system according to claim 3, which is characterized in that the model training unit, comprising:

Model training subelement, the simple electric financial product for being used based on epicycle training determine what wheel training used The argument value of the characteristic information of each simple electric financial product and the dependent variable value of the simple electric financial product, to participation At least two candidate families of epicycle training are trained；

Model determines subelement, for according to epicycle training as a result, from participate in epicycle training at least two candidate families in Determine the minimum candidate family of error rate；

Error product determines subelement, for determining the error simple electric of candidate family result mistake in epicycle training Financial product；

Weight updates subelement, for based on default weight update rule, the weight for the simple electric financial product that malfunctions to be updated, And the weight according to the error simple electric financial product updates, and updates the simple electric finance that the epicycle training uses and produces The weight of other simple electric financial products in product；

Stratified sampling handles subelement, for the present weight according to updated every simple electric financial product, to participation More simple electric financial products of epicycle training carry out stratified sampling processing, and obtaining next round training needs sample electricity to be used Sub- financial product, into next round training, it includes error sample that the next round training, which needs simple electric financial product to be used, This electronic banking product.

5. system according to claim 4, which is characterized in that the model determines that subelement is specifically used for:

Based on the historical transactional information of every simple electric financial product, the basic category of the affiliated company of simple electric financial product Property information and evaluation attributes information, determine the argument value of the current independent variable of the correspondence of the simple electric financial product and work as cause The dependent variable value of variable；

The correspondence argument value of every simple electric financial product is successively inputted at least two times that epicycle completes training respectively Modeling type, obtains whether the simple electric financial product occurs the high prediction result sent and turned；

According to the prediction result of each sample electronic banking product and the high record for sending and turning whether occurs, from participation epicycle training The minimum candidate family of error rate is determined at least two candidate families.

6. system according to claim 4 or 5, which is characterized in that the stratified sampling processing subelement is specifically used for:

From the error simple electric financial product, determine that present weight is greater than the error simple electric gold of default initial weight Melt product, as first sample electronic banking product；

It is needed using the first sample electronic banking product and the second simple electric financial product as next round training to be used Simple electric financial product.

7. system according to claim 4 or 5, which is characterized in that the model training module further include:

Weight determining unit determines wrong in epicycle model training for the default value relationship according to error rate and Model Weight The accidentally weight of the minimum candidate family of rate；Wherein, the default value relationship meets that error rate is smaller, and Model Weight is higher；

The prediction model generation unit is specifically used for the smallest model of error rate in each round model training and its corresponding mould The weighted array of type weight is sent as trained height and turns electronic banking product prediction model.

8. high the prediction technique for turning electronic banking product is sent a kind of characterized by comprising

The historical transactional information of every simple electric financial product is obtained, the basic category of the affiliated company of simple electric financial product Property information and evaluation attributes information, and history height corresponding to the simple electric financial product send and turn behavioural information；Wherein, institute State history height send change one's profession for information include whether occur height send turn record；

The historical transactional information, the essential attribute information and the evaluation attributes information are sent as height and turns electronic banking Whether the independent variable of product prediction model occurs high to send the record turned to send as the height to turn electronic banking product prediction using described The dependent variable of model, the training height, which is sent, turns electronic banking product prediction model；

It is sent based on the trained height and turns electronic banking product prediction model, prediction target electronic financial product generation height, which is sent, to be turned Result.

9. according to the method described in claim 8, it is characterized in that, the essential attribute information includes one of following information It is or a variety of: CompanyAddress's information, company size information, the affiliated trade information of company, associate's educational background information；The evaluation belongs to Property information include one of following information or a variety of: frequency information that company occurs in the stock, company occur in news Item number information, the number information that occurs in comment of company.

10. according to the method described in claim 8, it is characterized in that, the training height send and turns electronic banking product prediction Model, comprising:

The historical transactional information of every simple electric financial product based on acquisition, the affiliated company of simple electric financial product Essential attribute information and evaluation attributes information, determine the characteristic information of the simple electric financial product；

Using the characteristic information as independent variable, the record turned is sent to carry out at least one as dependent variable the corresponding height that whether occurs Take turns model training；Wherein, each round model training is participated in by least two candidate families；

By the combination of the smallest model of error rate in each round model training, send that turn electronic banking product pre- as trained height Survey model.