WO2020024718A1 - Procédé et dispositif de prédiction de volume de transaction en devises - Google Patents

Procédé et dispositif de prédiction de volume de transaction en devises Download PDF

Info

Publication number
WO2020024718A1
WO2020024718A1 PCT/CN2019/091735 CN2019091735W WO2020024718A1 WO 2020024718 A1 WO2020024718 A1 WO 2020024718A1 CN 2019091735 W CN2019091735 W CN 2019091735W WO 2020024718 A1 WO2020024718 A1 WO 2020024718A1
Authority
WO
WIPO (PCT)
Prior art keywords
foreign exchange
exchange transaction
transaction volume
business
data
Prior art date
Application number
PCT/CN2019/091735
Other languages
English (en)
Chinese (zh)
Inventor
杨永晟
Original Assignee
阿里巴巴集团控股有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 阿里巴巴集团控股有限公司 filed Critical 阿里巴巴集团控股有限公司
Publication of WO2020024718A1 publication Critical patent/WO2020024718A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Definitions

  • This specification relates to the field of data processing technology, and particularly to a method and device for predicting foreign exchange transaction volume.
  • this manual provides methods and devices for predicting foreign exchange transaction volume.
  • a method for predicting foreign exchange transaction volume includes: acquiring time series data, the time series data including a foreign exchange transaction business data sequence for multiple business days in a historical time period, The foreign exchange transaction business data sequence is arranged in chronological order, and each foreign exchange transaction business data sequence includes multiple dimensions of foreign exchange transaction business data within the same business day; inputting the time series data into a pre-trained multilayer convolutional neural network A model to predict the volume of foreign exchange transactions in the future time period.
  • the method further includes: calculating a prediction accuracy according to the predicted value and the real value of the foreign exchange transaction volume in the future time period; and modeling the model parameters of the multi-layer convolutional neural network model according to the prediction accuracy. Make adjustments.
  • the method further comprises: acquiring historical foreign exchange transaction volumes for several periods in history; calculating an average value and a variance of the foreign exchange transaction volumes in the same period; and anomaly the predicted value of the foreign exchange transaction volume according to the average value and the variance. Detection.
  • the method further comprises: if the predicted value of the foreign exchange transaction volume is less than X1, modifying the predicted value of the foreign exchange transaction volume to X1; if the predicted value of the foreign exchange transaction volume is greater than X2, modifying the The forecast value of foreign exchange transaction volume was revised to X2.
  • the step of inputting the time series data into a pre-trained multi-layer convolutional neural network model includes: calculating a foreign exchange transaction business feature of a key merchant based on the time series data; and entering a foreign exchange transaction business feature of the key merchant. Enter a pre-trained multilayer convolutional neural network model.
  • the foreign exchange transaction business data includes forward transaction volume, reverse transaction volume, business day date attributes, business day promotion attributes, and / or business day preset marketing amounts for each business day.
  • a foreign exchange transaction volume prediction device includes an acquisition module for acquiring time series data, and the time series data includes foreign exchange for multiple business days in a historical time period.
  • Transaction business data sequence the foreign exchange transaction data sequence is arranged in chronological order, and each foreign exchange transaction data sequence includes multiple dimensions of foreign exchange transaction business data within the same business day;
  • a prediction module is configured to convert the time series data Enter a pre-trained multi-layer convolutional neural network model to predict foreign exchange trading volume in the future time period.
  • a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the method according to any one of the embodiments is implemented.
  • a computer device including a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements any implementation when the program is executed.
  • Example method Example method.
  • FIG. 1 is a flowchart of a method for predicting foreign exchange transaction volume according to an embodiment of the present specification.
  • FIG. 2 is a schematic diagram of foreign exchange transaction data according to an embodiment of the present specification.
  • FIG. 3 is a schematic diagram of an abnormality detection process according to an embodiment of the present specification.
  • FIG. 4 is a block diagram of a foreign exchange transaction volume prediction device according to an embodiment of the present specification.
  • FIG. 5 is a schematic diagram of a computer device for implementing a method of an embodiment of the present specification according to an embodiment of the present specification.
  • first, second, third, etc. may be used in this specification to describe various information, the information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other.
  • first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information.
  • word “if” as used herein can be interpreted as “at” or "when” or "in response to determination”.
  • FIG. 1 it is a flowchart of a method for predicting foreign exchange transaction volume according to an embodiment of the present specification.
  • the method may include the following steps:
  • Step 102 Obtain time series data.
  • the time series data includes a foreign exchange transaction data sequence for multiple business days in a historical time period.
  • the foreign exchange transaction data sequence is arranged in chronological order, and each foreign exchange transaction business data sequence includes Data on foreign exchange transactions in multiple dimensions within the same business day;
  • Step 104 input the time series data into a pre-trained multilayer convolutional neural network to predict the foreign exchange transaction volume in the future time period.
  • time series data is first obtained, that is, a set of data arranged in chronological order.
  • the business day is a statistical period of business volume, which can be the same as the natural day (0:00 to 23:59), or can be preset by the business system. For example, it can be set to 15 of a natural day: 00 to 14:59 the next day, or set to another time period.
  • these dimensions may include forward transaction volume, reverse transaction volume, business day date attributes, business day promotion attributes, and / or business day preset marketing amounts for each business day.
  • these dimensions may include forward transaction volume, reverse transaction volume, business day date attributes, business day promotion attributes, and / or business day preset marketing amounts for each business day.
  • the user's purchase and payment behavior and the user's refund behavior bring about the opposite flow of funds.
  • the user's payment behavior is a forward transaction
  • the user's refund behavior is a Reverse transactions
  • the forward transaction volume is the total amount of the user's payment to the merchant
  • the reverse transaction volume is the total amount of the user's refund from the merchant.
  • the business day date attribute is whether the date corresponding to the business day is at the beginning of the month, the end of the month, the middle of the month, the working day, the non-working day, or the holiday.
  • the business day promotion attribute is whether or not there is a promotion activity and the activity level of the promotion activity on the business day. Among them, the activity level is determined by factors such as the discount strength of the promotion activity, the coverage of merchants, and the expected transaction volume.
  • the preset sales amount for a business day can be a preset sales amount for a single business day, and different business days can be set for different preset sales amounts.
  • FIG. 2 A schematic diagram of foreign exchange transaction data of an embodiment is shown in FIG. 2.
  • T represents the selected predicted time node
  • T + N represents the Nth business day after time node T.
  • step 104 the time series data obtained in step 102 may be input to a multi-layer convolutional neural network model, and the multi-layer convolutional neural network model is used to predict the foreign exchange transaction volume in the future time period.
  • Multi-layer convolutional neural network models can be pre-trained. During training, the time series data in the historical time period can be used as input, the real value of the foreign exchange transaction volume in the historical time period can be used as the output, and the model parameters of the multi-layer convolutional neural network model can be solved.
  • the multi-layer convolutional neural network model is used to solve the problem that conventional linear regression methods cannot mine the correlation between adjacent time nodes, and conventional time series processing methods cannot handle high-dimensional data nodes.
  • the multi-layer convolutional neural network can not only ensure the scalability of the high-dimensional features of each time node data, but also can ensure the sequential evolution between time nodes. At the same time, the neural network can control overfitting and automatically realize the selection of high-dimensional features. In addition, for refund and other services in foreign exchange transactions, the refund window is generally a fixed length. Convolutional neural networks train a relatively short amount of business window data and can capture the rules of short-term business windows.
  • the prediction accuracy may also be calculated according to the predicted value and the real value of the foreign exchange transaction volume in the future time period;
  • the model parameters of the multi-layer convolutional neural network model are adjusted. In this way, the model parameters of the multi-layer convolutional neural network model can be dynamically adjusted, and the prediction accuracy can be improved.
  • the method in the embodiment of the present specification further includes:
  • Step 302 Obtain the historical foreign exchange transaction volume for several periods in history
  • Step 304 Calculate the mean and variance of the foreign exchange transaction volume over the same period
  • Step 306 Anomaly detection is performed on the predicted value of the foreign exchange transaction volume according to the mean and variance. Through anomaly detection, abnormal predicted values can be filtered out, thereby further improving prediction accuracy.
  • the period in this embodiment may be a time period including multiple business days with the same business day date attribute. It can be a time period that includes multiple business day date attributes as the business day of the beginning of the month. For example, a cycle includes 3 months, and the three business days include three business day date attributes as the business day of the beginning of the month. It may also be a time period that includes multiple business day date attributes as Sunday business days. For example, a period includes 4 weeks, and these 4 weeks include 4 business day date attributes with Sunday business days.
  • the foreign exchange transaction volume is the foreign exchange transaction volume with the same business day and date attributes. For example, it can be two or more business day dates. The date attributes of two or more business days are the foreign exchange transaction volume of the business day at the beginning of the month.
  • Anomaly detection is performed on the predicted value of the foreign exchange transaction volume according to the mean and variance, and a numerical range of the foreign exchange transaction volume may be set in advance. If the predicted value of the foreign exchange transaction volume exceeds a preset numerical range, determining that the predicted value of the foreign exchange transaction volume is abnormal; wherein the numerical range may be:
  • ⁇ is the average value of the foreign exchange transaction volume in the same period
  • N is a preset positive integer
  • ⁇ is the variance of the foreign exchange transaction volume in the same period.
  • the predicted value of the foreign exchange transaction volume may be corrected to X1; if the predicted value of the foreign exchange transaction volume is greater than X2, the foreign exchange transaction volume may be predicted The value is corrected to X2.
  • the step of inputting the time series data into a pre-trained multi-layer convolutional neural network model includes: calculating a foreign exchange transaction business feature of a key merchant according to the time series data; Business feature input is a pre-trained multilayer convolutional neural network model.
  • the key merchants may be the merchants whose trading volume ranks among the top several in the entire currency's trading volume on that day, or the average value of their trading volume in the historical time period (for example, one month) among the top merchants.
  • a number of merchants can also be the top ones in terms of transaction volume on the same day, and the average number of merchants in the historical period of time also ranks in the top several merchants.
  • other features or a combination of features can also be used to determine whether a merchant is a key merchant.
  • the characteristics of foreign exchange transactions of key merchants may include characteristics such as the proportion of key merchant transaction volume, the intensity of key merchant activities, and / or the proportion of key merchants' high-quality goods.
  • the transaction volume ratio of key merchants is the ratio of the transaction volume of key merchants to the total transaction volume.
  • the intensity of key merchant activities is used to characterize which level of promotion activities held by key merchants are on the trading platform's promotional activities.
  • the level of activities can be divided according to the expected sales of key merchants.
  • the proportion of key merchandise's high-quality goods can be calculated based on the ratio of the sales of the merchandise rated above a certain rating value to the total sales of the key merchant.
  • an embodiment of the present specification further provides a device for predicting foreign exchange transaction volume.
  • the device may include:
  • the obtaining module 402 is configured to obtain time series data, where the time series data includes a foreign exchange transaction data sequence of multiple business days in a historical time period, the foreign exchange transaction data sequence is arranged in chronological order, and each foreign exchange transaction service The data sequence includes multiple dimensions of foreign exchange transaction business data within the same business day;
  • a prediction module 404 is configured to input the time series data into a pre-trained multi-layer convolutional neural network model to predict a foreign exchange transaction volume in a future time period.
  • the relevant part may refer to the description of the method embodiment.
  • the device embodiments described above are only schematic, and the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, which may be located in One place, or can be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in this specification. Those of ordinary skill in the art can understand and implement without creative efforts.
  • the embodiments of the apparatus of the present specification can be applied to a computer device, such as a server or a terminal device.
  • the device embodiments may be implemented by software, or by hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by reading the corresponding computer program instructions in the non-volatile memory into the memory and running the processor through the file processing processor.
  • FIG. 5 it is a hardware structure diagram of the computer equipment where the device of this specification is located, except for the processor 502, the memory 504, the network interface 506, and the non-volatile memory 508 shown in FIG. 5.
  • the server or electronic device where the device is located in the embodiment may generally include other hardware according to the actual function of the computer device, and details are not described herein again.
  • the embodiment of the present specification also provides a computer storage medium.
  • the storage medium stores a program, and when the program is executed by a processor, the method in any of the foregoing embodiments is implemented.
  • an embodiment of the present specification further provides a computer device including a memory, a processor, and a computer program stored on the memory and executable on the processor.
  • a computer device including a memory, a processor, and a computer program stored on the memory and executable on the processor.
  • the processor executes the program, any one of the foregoing embodiments is implemented.
  • the embodiments of the present specification may take the form of a computer program product implemented on one or more storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing program code.
  • Computer-usable storage media includes permanent and non-permanent, removable and non-removable media, and information can be stored by any method or technology.
  • Information may be computer-readable instructions, data structures, modules of a program, or other data.
  • Examples of computer storage media include, but are not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, Magnetic tape cartridges, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media may be used to store information that can be accessed by computing devices.
  • PRAM phase change memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • RAM random access memory
  • ROM read-only memory
  • EEPROM electrically erasable programmable read-only memory
  • flash memory or other memory technologies
  • CD-ROM compact disc read-only memory
  • DVD digital versatile disc
  • Magnetic tape cartridges magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media may be used to store information that can be accessed

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Finance (AREA)
  • Development Economics (AREA)
  • Physics & Mathematics (AREA)
  • Accounting & Taxation (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Technology Law (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne un procédé et un dispositif de prédiction d'un volume de transactions en devises. Le procédé consiste à : obtenir des données chronologiques consistant en séries de données commerciales de transaction en devises (102), chaque série de données commerciales de transaction en devises comprenant des données commerciales de transaction en devises de multiples dimensions dans la même journée commerciale ; puis saisir les données chronologiques dans un réseau neuronal convolutionnel multicouche pré-entraîné pour prédire un volume de transaction en devises dans une période à venir (104). L'influence mutuelle des données commerciales de transaction en devises entre des nœuds temporels adjacents est envisagée pendant la prédiction, et l'attribut caractéristique multidimensionnel d'un seul nœud temporel est utilisé, ce qui permet d'améliorer la précision de prédiction.
PCT/CN2019/091735 2018-08-03 2019-06-18 Procédé et dispositif de prédiction de volume de transaction en devises WO2020024718A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201810876277.9 2018-08-03
CN201810876277.9A CN109359758A (zh) 2018-08-03 2018-08-03 外汇交易量预测方法和装置

Publications (1)

Publication Number Publication Date
WO2020024718A1 true WO2020024718A1 (fr) 2020-02-06

Family

ID=65349814

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/091735 WO2020024718A1 (fr) 2018-08-03 2019-06-18 Procédé et dispositif de prédiction de volume de transaction en devises

Country Status (3)

Country Link
CN (1) CN109359758A (fr)
TW (1) TW202020781A (fr)
WO (1) WO2020024718A1 (fr)

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109359758A (zh) * 2018-08-03 2019-02-19 阿里巴巴集团控股有限公司 外汇交易量预测方法和装置
CN110163752A (zh) * 2019-04-16 2019-08-23 阿里巴巴集团控股有限公司 一种外汇交易量预测方法、装置及系统
CN110232437B (zh) * 2019-05-30 2021-11-16 湖南大学 基于cnn的时间序列预测方法和模型确定方法
CN110458664B (zh) * 2019-08-06 2021-02-02 上海新共赢信息科技有限公司 一种用户出行信息预测方法、装置、设备及存储介质
CN111222631A (zh) * 2020-01-17 2020-06-02 支付宝(杭州)信息技术有限公司 业务预测方法以及装置
CN113703974B (zh) * 2021-08-27 2024-06-25 深圳前海微众银行股份有限公司 一种预测服务器容量的方法及装置
CN113971495A (zh) * 2021-11-02 2022-01-25 中国银行股份有限公司 日间批量处理方法及装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106777874A (zh) * 2016-11-18 2017-05-31 中国科学院自动化研究所 基于循环神经网络构建预测模型的方法
CN107016571A (zh) * 2017-03-31 2017-08-04 北京百分点信息科技有限公司 数据预测方法及其系统
CN107705155A (zh) * 2017-10-11 2018-02-16 北京三快在线科技有限公司 一种消费能力预测方法、装置、电子设备及可读存储介质
CN108133391A (zh) * 2017-12-22 2018-06-08 联想(北京)有限公司 销量预测方法以及服务器
CN109359758A (zh) * 2018-08-03 2019-02-19 阿里巴巴集团控股有限公司 外汇交易量预测方法和装置

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107730052A (zh) * 2017-11-07 2018-02-23 中国农业科学院农业信息研究所 一种农产品价格预测方法及系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106777874A (zh) * 2016-11-18 2017-05-31 中国科学院自动化研究所 基于循环神经网络构建预测模型的方法
CN107016571A (zh) * 2017-03-31 2017-08-04 北京百分点信息科技有限公司 数据预测方法及其系统
CN107705155A (zh) * 2017-10-11 2018-02-16 北京三快在线科技有限公司 一种消费能力预测方法、装置、电子设备及可读存储介质
CN108133391A (zh) * 2017-12-22 2018-06-08 联想(北京)有限公司 销量预测方法以及服务器
CN109359758A (zh) * 2018-08-03 2019-02-19 阿里巴巴集团控股有限公司 外汇交易量预测方法和装置

Also Published As

Publication number Publication date
CN109359758A (zh) 2019-02-19
TW202020781A (zh) 2020-06-01

Similar Documents

Publication Publication Date Title
WO2020024718A1 (fr) Procédé et dispositif de prédiction de volume de transaction en devises
Elliott et al. Economic forecasting
US8577791B2 (en) System and computer program for modeling and pricing loan products
US11250499B2 (en) Generating optimal strategy for providing offers
US12093795B2 (en) Processing data inputs from alternative sources using a neural network to generate a predictive model for user stock recommendation transactions
EP2506207A1 (fr) Système informatique, procédé informatique et produit de programme informatique permettant de gérer les opérations d'un système
US11042930B1 (en) Insufficient funds predictor
TWI714113B (zh) 外匯交易量預測方法和裝置
US20090144123A1 (en) System and Method of Demand Modeling for Financial Service Products
JP2004528611A (ja) 構造化金融性能監視指標
CN116188061B (zh) 商品销量预测方法、装置、电子设备及存储介质
CN109583729B (zh) 用于平台在线模型的数据处理方法和装置
US20130226784A1 (en) System and method for credit balance transfer offer optimization
CN110135702A (zh) 一种实时更新的还款积极度评估方法、装置、系统和记录介质
US8255316B2 (en) Integrated business decision-making system and method
Tanaka Firm heterogeneity and FDI in distribution services
US20210150259A1 (en) Hierarchical deep neural network forecasting of cashflows with linear algebraic constraints
US20170061347A1 (en) Computerized system and method for predicting quantity levels of a resource
US20150088727A1 (en) Method for determining creditworthiness for exchange of a projected, future asset
Li et al. Predicting loss given default of unsecured consumer loans with time-varying survival scores
WO2024107733A1 (fr) Systèmes informatiques et procédés d'identification et de fourniture d'informations concernant des transactions récurrentes
CN116167646A (zh) 基于交易算法的评价方法、装置、设备及存储介质
JP2021502653A (ja) 目標の達成可能性に関する、可視表現の自動化された準備のためのシステム及び方法
US20240070552A1 (en) Methods and systems for determining payment behaviours
Brunel Lifetime PD Analytics for Credit Portfolios: A Survey

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19843256

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19843256

Country of ref document: EP

Kind code of ref document: A1