CN116257564A

CN116257564A - Asset combination scale prediction method, device, equipment and storage medium

Info

Publication number: CN116257564A
Application number: CN202211610348.3A
Authority: CN
Inventors: 谢长江
Original assignee: Ping An Bank Co Ltd
Current assignee: Ping An Bank Co Ltd
Priority date: 2022-12-14
Filing date: 2022-12-14
Publication date: 2023-06-13

Abstract

The utility model discloses a method, a device, equipment and a storage medium for predicting the scale of an asset combination, which are used for carrying out classification statistics on the asset combination of historical detail data under the preset classification dimension, predicting the predicted value of the asset combination of each preset classification dimension in a target time sequence based on a Prophet model and the asset combination balance value of the asset combination of each preset classification dimension under the known time sequence, improving the asset management and control capability and the fineness, identifying the potential risk in advance, identifying the key management and control asset combination of sudden increase and decrease of the balance of the asset combination, and providing index data of different analyses by the predicted values corresponding to different preset classification dimensions, thereby solving the technical problems that the asset combination management mainly depends on manual work and data statistics, cannot be finely managed and controlled and cannot be predicted for future trend.

Description

Asset combination scale prediction method, device, equipment and storage medium

Technical Field

The present disclosure relates to the field of financial technologies, and in particular, to a method, an apparatus, a device, and a storage medium for predicting a portfolio scale.

Background

The credit asset combination management is an indispensable means and method for managing credit assets by banks, and mainly aims to divide in-line asset business into different categories according to certain logic, and perform corresponding popularization and management and control measures aiming at the performance of the asset combination so as to properly bear risks to obtain benefits, and simultaneously ensure that the risks of the business are controllable and the benefits are maximized.

In the existing asset combination management, the management and control of assets are mainly carried out according to the experience of an expert by means of manual management and statistics of data; the method has the following defects:

1. the combination management depends on manual work, has thicker granularity, can not carry out fine management and control, and only focuses on;

2. policy and decision making mainly depend on the past data as references, but future trends and data cannot be acquired, decision lack basis, and efficient and accurate decision cannot be performed;

3. no prediction exists for future development of the existing combined asset, and the profit opportunity is missed;

4. the risk of the assets is not predicted and early-warned, the risk is not controlled in time, so that a plurality of assets are found only when thunderstorm occurs, the risk is not controlled in advance, and the loss degree is increased;

5. the scale development trend of some assets is not predicted, so that the development of the assets with policy trend in marketing is not optimistic, and part of potential assets are not supported by resources, so that the business scale and income are reduced.

Accordingly, it is desirable to provide a method for predicting the size of a portfolio to solve the above-mentioned problems.

Disclosure of Invention

The application provides a method, a device, equipment and a storage medium for predicting the scale of an asset combination, which solve the technical problems that the management of the asset combination mainly depends on the statistics of manpower and data, cannot be finely managed and controlled and cannot be predicted for future trends.

In view of this, a first aspect of the present application provides a method of portfolio scale prediction, the method comprising:

s1, acquiring historical detail data in a preset time period;

s2, classifying the asset combinations of the history detail data according to preset classification dimensions;

s3, counting the balance of the asset combination under each preset classification dimension;

s4, performing time sequence prediction on the balance of the asset combination in each preset classification dimension by adopting a Prophet model to obtain a predicted value of the asset combination in each preset classification dimension in a target time period.

Optionally, the step S1 specifically includes:

and taking the current time point as an end point, and acquiring historical detail data of 3-5 years forward.

Optionally, the preset classification dimension includes an institution type, a product type, and an industry type.

Optionally, the step S2 specifically includes:

marking the organization information, the product information and the industry information of the history detail data with type marks;

and classifying the asset combinations of the historical detail data according to the preset classification dimension and the type identifier.

Optionally, the step S4 specifically includes:

s41, constructing a Prophet model;

s42, taking balance of the asset combination and corresponding time information of the asset combination in each preset classification dimension as first input parameters, and inputting a target time period as second input parameters into the propset model to conduct time sequence prediction, so as to obtain a predicted value of the asset combination in each preset classification dimension in the target time period, wherein the predicted value comprises an index value, a maximum value and a minimum value.

Optionally, the step S4 further includes:

and generating a statistical map of the asset combination under each preset classification dimension according to the predicted value, wherein the statistical map comprises a fitting curve, an upper limit and a lower limit.

Optionally, the step S4 further includes:

and comparing the predicted value with a preset target value, and generating early warning information according to a comparison result.

A second aspect of the present application provides a portfolio scale prediction apparatus, the apparatus comprising:

the acquisition unit is used for acquiring historical detail data in a preset time period;

the classification unit is used for classifying the asset combinations of the history detail data according to a preset classification dimension;

the statistics unit is used for counting the balance of the asset combination under each preset classification dimension;

the prediction unit is used for carrying out time sequence prediction on the balance of the asset combination in each preset classification dimension by adopting a Prophet model to obtain a predicted value of the asset combination in each preset classification dimension in a target time period.

A third aspect of the present application provides a portfolio scale prediction apparatus, the apparatus comprising a processor and a memory:

the memory is used for storing program codes and transmitting the program codes to the processor;

the processor is configured to perform the steps of the method of portfolio scale prediction as described in the first aspect above, according to instructions in the program code.

A fourth aspect of the present application provides a computer readable storage medium for storing program code for performing the method of the first aspect described above.

From the above technical solutions, the embodiments of the present application have the following advantages:

according to the asset combination scale prediction method, device and equipment and storage medium, classification statistics of asset combinations are carried out on historical detail data under preset classification dimensions, the predicted value of the asset combinations of each preset classification dimension in a target time sequence is predicted based on a Prophet model and asset combination balance values of the asset combinations of each preset classification dimension under a known time sequence, asset management and control capability and fineness are improved, potential risks can be recognized in advance, key management and control asset combinations of sudden increase and decrease of the asset combinations balance are recognized, index data of different analyses can be provided by the predicted values corresponding to different preset classification dimensions, and the technical problems that management of the asset combinations is mainly dependent on manual work and statistics of data, cannot be finely managed and controlled, and future trends cannot be predicted are solved.

Drawings

FIG. 1 is a method flow diagram of a portfolio scale prediction method in an embodiment of the present application;

FIG. 2 is a schematic structural diagram of a device for predicting the size of a portfolio according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a device for predicting a combined asset scale according to an embodiment of the present application.

Detailed Description

In order to make the present application solution better understood by those skilled in the art, the following description will clearly and completely describe the technical solution in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.

The application designs a method, a device, equipment and a storage medium for predicting the scale of an asset combination, which solve the technical problems that the management of the asset combination mainly depends on the statistics of manpower and data, cannot be finely managed and controlled and cannot be predicted for future trends.

For ease of understanding, referring to fig. 1, fig. 1 is a flowchart of a method for predicting a portfolio scale according to an embodiment of the present application, as shown in fig. 1, specifically:

s1, acquiring historical detail data in a preset time period;

specifically, the current time point is taken as an end point, and historical detail data of 3-5 years in the front is obtained.

It should be noted that, according to expert experience, the economic short-term period takes 3-5 years as a fluctuation period, and is also a data storage basis in a common line, and the data which can be traced back is usually 3-5 years; therefore, the accuracy is evaluated based on the basis as the data reference, so that the adaptability and the accuracy of the prediction are improved. Typically, 3-5 years of historical borrowing details are obtained, including institutional information, product information, and industry information, e.g., institutional information is divided into general lines or sub-lines, even sub-lines of specific interest; the product information is divided into information of loan products such as white collar loans, car loans, personal house loans and the like; industry information is classified according to the national standard four-level industry.

S2, classifying asset combinations of the historical detail data according to preset classification dimensions;

specifically, the preset classification dimension includes an organization type, a product type, and an industry type;

and classifying the asset combinations of the historical detail data according to the preset classification dimension and the type identification.

It should be noted that, a piece of history detail data may include information of multiple classification dimensions, for example:

the first piece of historical detail data is characterized in that the organization information of the first piece of historical detail data is a headquarter, the product information is a personal house credit, and the industry type is real estate;

the organization information of the second historical detail data is a branch, the product information is a personal house credit, and the industry type is real estate.

When type identification marking processing is carried out on the two pieces of history detail data, a first meeting is endowed with a corresponding label of a headquarter, a corresponding label of a personal house loan and a corresponding label of a real estate; and a second party is provided with a branch corresponding label, a personal house loan corresponding label and a real estate corresponding label.

When the asset combination classification is performed on two pieces of history detail data according to the preset classification dimension and the type identifier, it is obvious that if the asset combination is classified according to the organization type, the two pieces of history detail data are not placed in the same classification pool, but if the asset combination is classified according to the product type or the industry type, the two pieces of history detail data are placed in the same classification pool.

it should be noted that, for the balance of the portfolio in the same preset classification dimension, the balance of the portfolio in each history detail data is accumulated to be the balance of the portfolio in the preset classification dimension.

S4, performing time sequence prediction on the balance of the asset combination under each preset classification dimension by adopting a Prophet model to obtain a predicted value of the asset combination under each preset classification dimension in a target time period.

Specifically:

constructing a Prophet model;

the balance of the asset combination and corresponding time information of the asset combination in each preset classification dimension are used as first input parameters, a target time period is used as second input parameters to be input into a propset model for time sequence prediction, and a predicted value of the asset combination in each preset classification dimension in the target time period is obtained, wherein the predicted value comprises an index value, a maximum value and a minimum value;

it should be noted that, the Prophet model has higher accuracy in the prediction of time series, and can output the index value, the maximum value and the minimum value of a longer target time in the future, and has higher operability in the prediction of the asset combination scale prediction index.

For example, the balance of the portfolio in a preset classification dimension over a historical time sequence is used as input data:

date (time)	AssetValue (asset value)
		2022 month 1	200
2022 month 2	203
		2022 month 3	207
2022 month 4	202

In combination with the target time period entered into the Prophet model, one can get:

FDate (time)	FAssetValue (asset value)	Flower	Fupper
				2022 month 5	205	204	206
2022 month 6	206	204	208
				2022, 7	208	205	210

The possible range of the prediction result can be expanded with time sequence, so that the data of 3-6 months of prediction is generally selected in the selection of the prediction data, thereby meeting the management requirement and simultaneously considering the accuracy.

Further comprises:

It should be noted that, according to the output predicted value, a statistical map of the asset combination under each preset classification dimension may also be generated, where the statistical map includes a fitted curve, an upper limit and a lower limit.

Further comprises:

s5, comparing the predicted value with a preset target value, and generating early warning information according to the comparison result.

The prediction result is compared with the annual target value in the management of the row, and an alarm is generated.

The annual target value is mainly managed by a scale limit, namely the scale of a certain type of assets cannot exceed the proportion of the total assets, so that risks are balanced, and concentration risks are prevented. Wherein the annual target value is set for the annual intra-row strategy determination and is monitored in linear divisions to each month.

For example: the real estate asset scale in a row cannot exceed 10%, the scale is converted into 120 hundred million asset scale, the first month is 10 hundred million, the second month is 20 hundred million, and the like, and the floating range is set at the same time, and the floating is 10%; that is, the first month exceeds 11 hundred million, and the early warning is generated and the control means is included.

According to the technical scheme, the efficiency of in-line resource combination management and the fineness of management and control can be obviously improved, and the system can automatically early warn by traditional manual statistics; the asset configuration strategy can be guided by predicting and guiding the issuing of credit policies, distinguishing potential and quality assets; potential risks can be identified in advance, assets with scale increase, explosion and subtraction are brought into important control in advance, and losses are reduced. The scale trend of the assets in the line can be estimated, the layout and preparation of the assets in the line can be decided, and the asset benefits can be improved.

Referring to fig. 2, fig. 2 is a schematic structural diagram of a device for predicting a combined asset scale according to an embodiment of the present application, and as shown in fig. 2, the device specifically includes:

an acquisition unit 201, configured to acquire history detail data in a preset period;

a classification unit 202, configured to classify the asset combinations of the history detail data according to a preset classification dimension;

a statistics unit 203, configured to count a balance of an asset combination in each preset classification dimension;

and the prediction unit 204 is configured to perform time-series prediction on the balance of the portfolio in each preset classification dimension by using a Prophet model, so as to obtain a predicted value of the portfolio in each preset classification dimension in the target time period.

Further, the acquiring unit 201 is specifically configured to acquire historical detail data of 3-5 years forward with the current time point as an end point.

Further, the preset classification dimensions include institution type, product type, and industry type.

Further, the classification unit 202 is specifically configured to:

Further, the prediction unit 204 is specifically configured to:

constructing a Prophet model;

and (3) taking the balance of the asset combination and corresponding time information of the asset combination in each preset classification dimension as a first input parameter, and inputting a target time period as a second input parameter into a Prophet model for time sequence prediction to obtain a predicted value of the asset combination in each preset classification dimension in the target time period, wherein the predicted value comprises an index value, a maximum value and a minimum value.

Further, the prediction unit 204 is further configured to generate a statistical map of the portfolio in each preset classification dimension according to the predicted value, where the statistical map includes a fitted curve, an upper limit and a lower limit.

Further, the method further comprises the following steps:

and the early warning unit is used for comparing the predicted value with a preset target value and generating early warning information according to the comparison result.

The embodiment of the present application further provides another equipment for predicting the combined asset scale, as shown in fig. 3, for convenience of explanation, only the parts related to the embodiment of the present application are shown, and specific technical details are not disclosed, please refer to the method part of the embodiment of the present application. The terminal can be any terminal equipment including a mobile phone, a tablet personal computer, a personal digital assistant (English full name: personal Digital Assistant, english abbreviation: PDA), a Sales terminal (English full name: point of Sales, english abbreviation: POS), a vehicle-mounted computer and the like, taking the mobile phone as an example of the terminal:

fig. 3 is a block diagram showing a part of a structure of a mobile phone related to a terminal provided in an embodiment of the present application. Referring to fig. 3, the mobile phone includes: radio Frequency (RF) circuit 1010, memory 1020, input unit 1030, display unit 1040, sensor 1050, audio circuit 1060, wireless fidelity (wireless fidelity, wiFi) module 1070, processor 1080, and power source 1090. Those skilled in the art will appreciate that the handset configuration shown in fig. 3 is not limiting of the handset and may include more or fewer components than shown, or may combine certain components, or may be arranged in a different arrangement of components.

The following describes the components of the mobile phone in detail with reference to fig. 3:

the RF circuit 1010 may be used for receiving and transmitting signals during a message or a call, and particularly, after receiving downlink information of a base station, the signal is processed by the processor 1080; in addition, the data of the design uplink is sent to the base station. Generally, RF circuitry 1010 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (English full name: low Noise Amplifier, english abbreviation: LNA), a duplexer, and the like. In addition, the RF circuitry 1010 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to global system for mobile communications (english: global System of Mobile communication, english: GSM), general packet radio service (english: general Packet Radio Service, GPRS), code division multiple access (english: code Division Multiple Access, english: CDMA), wideband code division multiple access (english: wideband Code Division Multiple Access, english: WCDMA), long term evolution (english: long Term Evolution, english: LTE), email, short message service (english: short Messaging Service, SMS), and the like.

The memory 1020 may be used to store software programs and modules that the processor 1080 performs various functional applications and data processing of the handset by executing the software programs and modules stored in the memory 1020. The memory 1020 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, memory 1020 may include high-speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state memory device.

The input unit 1030 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the handset. In particular, the input unit 1030 may include a touch panel 1031 and other input devices 1032. The touch panel 1031, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on the touch panel 1031 or thereabout using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a predetermined program. Alternatively, the touch panel 1031 may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device and converts it into touch point coordinates, which are then sent to the processor 1080 and can receive commands from the processor 1080 and execute them. Further, the touch panel 1031 may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. The input unit 1030 may include other input devices 1032 in addition to the touch panel 1031. In particular, other input devices 1032 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a track ball, a mouse, a joystick, etc.

The display unit 1040 may be used to display information input by a user or information provided to the user and various menus of the mobile phone. The display unit 1040 may include a display panel 1041, and alternatively, the display panel 1041 may be configured in the form of a liquid crystal display (english full name: liquid Crystal Display, acronym: LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch panel 1031 may overlay the display panel 1041, and when the touch panel 1031 detects a touch operation thereon or thereabout, the touch panel is transferred to the processor 1080 to determine a type of touch event, and then the processor 1080 provides a corresponding visual output on the display panel 1041 according to the type of touch event. Although in fig. 3, the touch panel 1031 and the display panel 1041 are two independent components for implementing the input and output functions of the mobile phone, in some embodiments, the touch panel 1031 and the display panel 1041 may be integrated to implement the input and output functions of the mobile phone.

The handset may also include at least one sensor 1050, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 1041 according to the brightness of ambient light, and the proximity sensor may turn off the display panel 1041 and/or the backlight when the mobile phone moves to the ear. As one of the motion sensors, the accelerometer sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and direction when stationary, and can be used for applications of recognizing the gesture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that may also be configured with the handset are not described in detail herein.

Audio circuitry 1060, a speaker 1061, and a microphone 1062 may provide an audio interface between a user and a cell phone. Audio circuit 1060 may transmit the received electrical signal after audio data conversion to speaker 1061 for conversion by speaker 1061 into an audio signal output; on the other hand, microphone 1062 converts the collected sound signals into electrical signals, which are received by audio circuit 1060 and converted into audio data, which are processed by audio data output processor 1080 for transmission to, for example, another cell phone via RF circuit 1010 or for output to memory 1020 for further processing.

WiFi belongs to a short-distance wireless transmission technology, and a mobile phone can help a user to send and receive emails, browse webpages, access streaming media and the like through a WiFi module 1070, so that wireless broadband Internet access is provided for the user. Although fig. 3 shows a WiFi module 1070, it is understood that it does not belong to the necessary constitution of the handset, and can be omitted entirely as required within the scope of not changing the essence of the invention.

Processor 1080 is the control center of the handset, connects the various parts of the entire handset using various interfaces and lines, and performs various functions and processes of the handset by running or executing software programs and/or modules stored in memory 1020, and invoking data stored in memory 1020, thereby performing overall monitoring of the handset. Optionally, processor 1080 may include one or more processing units; preferably, processor 1080 may integrate an application processor primarily handling operating systems, user interfaces, applications, etc., with a modem processor primarily handling wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 1080.

The handset further includes a power source 1090 (e.g., a battery) for powering the various components, which may preferably be logically connected to the processor 1080 by a power management system, such as to provide for managing charging, discharging, and power consumption by the power management system.

Although not shown, the mobile phone may further include a camera, a bluetooth module, etc., which will not be described herein.

In the embodiment of the present application, the processor 1080 included in the terminal further has the following functions:

s1, acquiring historical detail data in a preset time period;

The present embodiments also provide a computer readable storage medium storing program code for performing any one of the foregoing methods of predicting a portfolio size of the respective embodiments.

In the embodiment of the application, the method, the device, the equipment and the storage medium for predicting the scale of the asset combination are provided, the asset combination balance value of the asset combination of each preset classification dimension in a target time sequence is predicted based on the propset model and the asset combination balance value of the asset combination of each preset classification dimension in a known time sequence by carrying out classification statistics on historical detail data under the preset classification dimension, the asset management and control capability and the fineness of the asset combination are improved, potential risks can be identified in advance, the key management and control asset combination of the increase, the storm and the decrease of the asset combination balance can be identified, the predicted values corresponding to different preset classification dimensions can also provide index data of different analyses, and the technical problems that the asset combination management mainly depends on manual and data statistics and cannot be finely managed and controlled and cannot be predicted for future trends are solved.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

The terms "first," "second," "third," "fourth," and the like in the description of the present application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be capable of operation in sequences other than those illustrated or described herein, for example. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be understood that in this application, "at least one" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.

In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.

The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims

1. A method of portfolio scale prediction, comprising:

s1, acquiring historical detail data in a preset time period;

2. The method for predicting the combined asset size according to claim 1, wherein the step S1 specifically includes:

3. The portfolio scale prediction method of claim 1, wherein the preset classification dimensions comprise a institutional type, a product type, and an industry type.

4. The method for predicting the combined asset size according to claim 3, wherein said step S2 specifically comprises:

5. The method of claim 1, wherein the step S4 specifically includes:

s41, constructing a Prophet model;

6. The portfolio scale prediction method of claim 5, wherein step S4 further comprises:

7. The method of claim 5, wherein the step S4 further comprises:

8. A portfolio scale prediction apparatus, comprising:

9. A portfolio scale prediction apparatus, the apparatus comprising a processor and a memory:

the processor is configured to perform the portfolio scale prediction method of any one of claims 1-7 in accordance with instructions in the program code.

10. A computer readable storage medium storing program code for performing the portfolio scale prediction method of any one of claims 1-7.