CN114462686A - Deposit estimation method, deposit estimation device, nonvolatile storage medium and electronic equipment - Google Patents
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Abstract
The invention discloses a deposit estimation method and device, a nonvolatile storage medium and electronic equipment, which are applied to the field of financial science and technology. Wherein, the method comprises the following steps: the method comprises the steps of obtaining historical data of a deposit, wherein the historical data is a time sequence of the deposit in a continuous time period, estimating parameters in a deposit autoregressive model according to the historical data of the deposit, generating the deposit autoregressive model, converting the deposit autoregressive model to obtain a deposit state space model, and estimating the deposit data at a preset moment based on the historical data of the deposit and the deposit state space model, wherein the preset moment is a moment after the continuous time period. The invention solves the technical problem of inaccurate estimation of the deposit amount at the future time in the banking institution.
Description
Technical Field
The invention relates to the field of financial science and technology, in particular to a deposit estimation method and device, a nonvolatile storage medium and electronic equipment.
Background
For commercial banks, the current deposit is one of the main fund sources, and the prediction of the development trend of the deposit of all depositors in the commercial banks is beneficial to arranging and using the fund in advance, so that the management efficiency of banking business is improved, and the overall competitiveness is improved.
In the related art, bank deposit estimation methods such as a time series prediction method based on wiener prediction are proposed, and the method achieves the purpose of predicting the future trend through self explanation and error correction of a single variable. The process generally has two processes: autoregressive process and moving average process, two indexes: autocorrelation coefficients and partial autocorrelation coefficients. The modeling, estimation and inspection method provided by the theory is most universal in the financial field. In addition, a neural Network prediction method can be adopted, the neural Network prediction method is a Network-based 'push-Back' learning algorithm, in the financial field, a Back Propagation Network (BP neural Network) is usually adopted for predicting the development trend of financial data, and the BP neural Network is a feedforward type neural Network algorithm.
However, the two methods have respective disadvantages, the wiener prediction method is based on the stationary time sequence for prediction, and considering from the algorithm source, the method mainly solves the wiener-hough equation on the basis of the system transfer function, cannot solve the prediction problem of the non-stationary time sequence, generally needs to adopt more approximate processing to a financial system, has insufficient prediction precision and overlarge calculated amount, and is not suitable for large-scale numerical calculation. The BP neural network prediction method is essentially to train a mathematical model based on a kernel function according to enough observation data and depending on the selected kernel function to obtain a prediction equation. Obviously, the method has a large dependency on the selection of the kernel function, and if the kernel function is selected incorrectly, the output of the final prediction equation diverges. In addition, the BP neural network needs to specify parameters such as the number of neural network layers and the number of nodes, which are closely related to the accuracy and the computational complexity of the algorithm, and the adjustment period is long, which is not favorable for practical operation.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a deposit estimation method and device, a nonvolatile storage medium and electronic equipment, which are used for at least solving the technical problem of inaccurate estimation of deposit amount at a future moment in a banking institution.
According to an aspect of an embodiment of the present invention, there is provided a deposit estimation method including: acquiring historical data of the deposit, wherein the historical data is a time sequence of the deposit in continuous time periods; estimating parameters in a deposit autoregressive model according to the historical data of deposits to generate the deposit autoregressive model; converting the deposit autoregressive model to obtain a deposit state space model; estimating deposit data at a predetermined time based on the historical data of the deposit and the deposit state space model, wherein the predetermined time is a time after the continuous time period.
Optionally, the estimating parameters in a deposit autoregressive model according to the historical data of the deposit, and generating the deposit autoregressive model includes: and estimating parameters in the deposit autoregressive model by adopting a moment estimation method based on the historical data to obtain the deposit autoregressive model.
Optionally, the deposit autoregressive model comprises:
s(k)+A1s(k-1)+…+Ans(k-n)＝C0e(k)+C1e(k-1)+…+Cne(k-n)wherein s is(i)Indicating deposit data at time i, e(i)Representing the model error at time i, AiAnd CiA matrix of m x m representing parameters of the deposit autoregressive model, the ith time being within the continuous time period.
Optionally, the deposit state space model includes: determining a spatial state vector of a k-th moment based on deposit data, a system observation matrix and system observation noise of the k-th moment, wherein the k-th moment is within the continuous time period; and determining the space state vector at the k +1 th moment based on the space state vector at the k th moment, the system transfer matrix and the system interference noise.
Optionally, the space state vector at the k-th time is determined based on the deposit data at the k-th time, a system observation matrix and system observation noise, and is determined by the following formula:
wherein s is(k)For the deposit data at the k-th time, [ I ]m Om … Om]For the system observation matrix, C0e(k)The noise is observed for the system in question,
a spatial state vector representing the k-th time instant; the determining the spatial state vector at the k +1 th time based on the spatial state vector at the k th time, the system transfer matrix and the system interference noise includes:
wherein,
representing the system transfer matrix of the system,
representing system interference noise.
Optionally, the estimating the deposit data at a predetermined time based on the historical data of the deposit and the deposit state space model includes: generating a deposit estimation algorithm based on the deposit state space model and an energy gain minimum criterion in Krein space estimation; and determining the deposit data at the preset moment according to the historical data of the deposit and the deposit estimation algorithm.
Optionally, the energy gain minimization criterion in the Krein spatial estimation comprises:
According to another aspect of the embodiments of the present invention, there is also provided a deposit estimation device including: the acquisition module is used for acquiring historical data of the deposit, wherein the historical data is a time sequence of the deposit in continuous time periods; the generating module is used for estimating parameters in a deposit autoregressive model according to the historical data of deposits and generating the deposit autoregressive model; the conversion module is used for converting the deposit autoregressive model to obtain a deposit state space model; and the estimation module is used for estimating deposit data at a preset time based on the historical data of the deposit and the deposit state space model, wherein the preset time is a time after the continuous time period.
According to another aspect of the embodiment of the present invention, there is also provided a non-volatile storage medium, which includes a stored program, wherein when the program runs, a device in which the non-volatile storage medium is located is controlled to execute any one of the above deposit estimation methods.
According to still another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor, where the processor is configured to execute a program, where the program executes the method for estimating the amount.
In the embodiment of the invention, the historical data of the deposit is adopted to estimate the parameters in the deposit autoregressive model, the deposit autoregressive model is generated, the deposit state space model is obtained by converting the deposit autoregressive model, and then the deposit data at the preset moment is estimated based on the historical data of the deposit and the deposit state space model, so that the aim of estimating the deposit amount of the bank with high precision is fulfilled, the technical effect of improving the estimation precision of the deposit data at the future moment in the bank mechanism is realized, and the technical problem of inaccurate estimation of the deposit amount at the future moment in the bank mechanism is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 shows a block diagram of a hardware structure of a computer terminal for implementing a deposit estimation method;
FIG. 2 is a flow chart illustrating a credit estimation method according to an embodiment of the invention;
FIG. 3 is a flow diagram providing prediction using a deposit state space model in accordance with an alternative embodiment of the present invention;
fig. 4 is a block diagram of a structure of a deposit estimation device provided according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. 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.
First, partial terms or terms appearing in the description of the embodiments of the present application are applied to the following explanations:
an autoregressive model (AR model) is a model that statistically processes time series, and predicts the performance of the current stage by using the performance of each stage before the same variable, and assumes a linear relationship.
The state space model is a dynamic time domain model, the implicit time is used as an independent variable, and a state space expression can be used for completely expressing a control system in a state space.
In accordance with an embodiment of the present invention, there is provided a method embodiment of credit estimation, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that presented herein.
The method provided by the first embodiment of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Fig. 1 shows a block diagram of a hardware configuration of a computer terminal for implementing a deposit estimation method. As shown in fig. 1, the computer terminal 10 may include one or more processors (shown as 102a, 102b, … …, 102 n) which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, or the like, a memory 104 for storing data. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial BUS (USB) port (which may be included as one of the ports of the BUS), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
It should be noted that the one or more processors and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuit may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computer terminal 10. As referred to in the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).
The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the deposit estimation method in the embodiment of the present invention, and the processor executes various functional applications and data processing by operating the software programs and modules stored in the memory 104, that is, implementing the above-mentioned deposit estimation method for the application program. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with the user interface of the computer terminal 10.
Fig. 2 is a schematic flow chart of a deposit estimation method according to an embodiment of the present invention, as shown in fig. 2, the method includes the following steps:
step S202, acquiring historical data of the deposit, wherein the historical data is a time sequence of the deposit in continuous time periods. In this step, the historical data of the deposit may be the daily deposit data of the bank in the previous month, the deposit data of the month constitutes a time series, and the deposit amount is a variable in the time series.
And step S204, estimating parameters in the deposit autoregressive model according to the historical data of the deposit, and generating the deposit autoregressive model.
And step S206, converting the deposit autoregressive model to obtain a deposit state space model. In this step, after the mathematical equation of the deposit autoregressive model is obtained, the deposit autoregressive model can be converted into a deposit state space model according to the method of converting the autoregressive equation into a state space equation. Alternatively, the deposit state space model here is an initial model that can recursively predict the deposit data of the bank.
Note that, in the deposit autoregressive model, e(k)The model error is large, and the influence of the error statistic on the prediction result is large, so that the estimation precision of the deposit is difficult to improve. In the deposit status space, e(k)The method is a control quantity, and the size of the control quantity can be controlled, so that the estimation error can be reduced, and the estimation precision of the deposit can be improved.
And step S208, estimating deposit data at a preset time based on the historical data of the deposit and the deposit state space model, wherein the preset time is a time after a continuous time period. Alternatively, the deposit state space model may be an initial model for recursively predicting deposit data, and the value at a future predetermined time, that is, the estimated value of the deposit data at the predetermined time, may be predicted by inputting the historical data of the deposit into the initial model and performing recursive iteration.
Through the steps, parameters in the deposit autoregressive model are estimated by adopting the historical data of the deposit, the deposit autoregressive model is generated, the deposit state space model is obtained through conversion of the deposit autoregressive model, then the deposit data at the preset moment are estimated based on the historical data of the deposit and the deposit state space model, and the purpose of estimating the deposit amount of the bank with high precision is achieved, so that the technical effect of improving the estimation precision of the deposit data at the future moment in the bank mechanism is achieved, and the technical problem that the estimation of the deposit amount at the future moment in the bank mechanism is inaccurate is solved.
As an alternative embodiment, the deposit autoregressive model is generated by estimating parameters in the deposit autoregressive model according to historical data of the deposit, and the deposit autoregressive model can be obtained by estimating the parameters in the deposit autoregressive model by a moment estimation method based on the historical data.
The moment estimation method can estimate the overall corresponding parameters by using the sample moment, and can estimate the parameters according to the observation sequence { si}i＝0,1,2,…kEstimate AiAnd CiAnd obtaining a deposit autoregression model.
As an alternative embodiment, the deposit autoregressive model comprises:
s(k)+A1s(k-1)+…+Ans(k-n)＝C0e(k)+C1e(k-1)+…+Cne(k-n)wherein s is(i)Indicating deposit data at time i, e(i)Representing the model error at time i, AiAnd CiA matrix of m x m representing the parameters of the deposit autoregressive model, the ith time being in a continuous period of time.
In the above formula, s(k)∈Rm，e(k)∈Rm，AiAnd CiIs an m × m matrix, s(k)For observable deposit data at time k for the bank, e(k)Is the model error.
As an alternative embodiment, the deposit state space model includes: determining a space state vector of the kth moment based on the deposit data, the system observation matrix and the system observation noise of the kth moment, wherein the kth moment is positioned in a continuous time period; and determining the space state vector at the k +1 th moment based on the space state vector at the k th moment, the system transfer matrix and the system interference noise.
The deposit state space model can be an initial model of recursive prediction, and the model predictor can predict a space state vector at the k +1 moment based on the space state vector at the k +1 moment and obtain a deposit prediction value at the k +1 moment, namely deposit estimation data at the k +1 moment based on the space state vector at the k +1 moment.
As an alternative embodiment, the spatial state vector at the k-th time is determined based on the deposit data at the k-th time, the system observation matrix and the system observation noise, and is determined by the following formula:
wherein s is(k)Deposit data at the k-th time, [ I ]m Om … Om]For systematic observation of the matrix, C0e(k)In order for the system to observe the noise,
representing the spatial state vector at the k-th time instant.
Determining the spatial state vector at the k +1 th time based on the spatial state vector at the k th time, the system transition matrix and the system interference noise may be performed as follows:
For the convenience of subsequent use, the above contents can be simplified as follows:
in the above formula, the state vector:
a system transfer matrix:
system interference noise:
system observation matrix: h(k)＝[Im Om … Om]，
The system observes noise: v. of(k)＝C0e(k)System observation value: y isk＝s(k). The system is a system corresponding to the deposit state space model.
Based on the prediction method that the deposit state space model meets the standard, the prediction can be realized by using the prediction equation determined according to the deposit state space model.
Alternatively, the prediction equation in the deposit state space model may be determined as follows. As an alternative embodiment, estimating the deposit data at a predetermined time based on the historical data of the deposit and the deposit state space model includes: generating a deposit estimation algorithm based on a deposit state space model and an energy gain minimum criterion in Krein space estimation; determining deposit data at a predetermined time based on the historical data of the deposit and based on a deposit estimation algorithm.
The Krein space theory can be used for estimation optimization of a state space, reduces errors in an original system, and improves estimation precision. Specifically, for the linear random state space mathematical model, according to the Krein space estimation theory, the estimation accuracy of the system is the highest if and only if the following formula of the energy gain minimum criterion is satisfied. The energy gain minimum criterion inequality in the Krein spatial estimation is:
wherein sup denotes supremum, x0Representing the initial state of the model, ukRepresenting model noise at time k, vkRepresenting the observed noise at time k, h2The representation of the hubert space is,
which represents the X estimate at time i,
x representing the ith time0Estimate value, UiRepresenting the variance, R, of the model noise u at the i-th momentiRepresents the variance of model noise v at the ith time, gamma represents the index to be estimated at the kth time, PiRepresenting the variance of the estimation error at time i.
Further, based on the Krein space estimation theory and the above criteria, the following estimation algorithm is easily derived:
Pk|k-1＝Fk|k-1Pk-1|k-1Fk|k-1+Uk
where P represents the variance of the state x, U represents the variance of the system noise w, R represents the variance of the system noise v, U represents the mean of the system noise w, R represents the mean of the observed noise v, R represents the mean of the observed noise veExpressing observation information variance, alpha expressing gain constant (alpha > 1), comprehensively determining according to precision requirement and stability index of predictor for deposit estimation, determining numerical value by adopting exhaustion method, eig expressing matrix eigenvalue operation, max { eig } expressing maximum eigenvalue operation,
an estimated value of X is represented by,
an estimate of x is represented by a value,
indicating deposit data at the k +1 th time estimated based on the k-th time.
To this end
Namely the observed value to be predicted, namely the deposit data at the k +1 th time.
In the process of the algorithm, the deposit data at the next moment is calculated based on the deposit state space model in the deposit state space, and the controllability and observability of the system can be ensured by adjusting the value of alpha under the condition of not acquiring accurate statistical information of system noise and observation noise.
Compared with the method in the related art, the method provided by the embodiment has the following advantages: compared with the traditional wiener prediction method based on the autoregressive moving average model, the method provided by the embodiment does not need to solve a complex wiener Hough equation, the calculated amount is greatly reduced, and because the method does not strictly require that system noise and observation noise are both white noise, in practical application (the actual noise is mostly non-white noise), the prediction precision is improved, and the method is more suitable for the prediction of an actual financial system.
Compared with a neural network prediction method, a kernel function does not need to be selected firstly, and parameters such as neural network nodes and gradient parameters which influence the precision of the prediction algorithm in a polar manner do not need to be estimated through complex and massive calculations, so that the calculation efficiency and the calculation precision are improved.
Fig. 3 is a flow chart of prediction using a deposit state space model according to an alternative embodiment of the present invention, as shown in fig. 3, the process including the steps of:
step S1, input: deposit data { sk, sk-1, … …, s0}, index α, initial system variance P0|0, initial system state x0| 0;
step S2, calculating to obtain an autoregressive model based on an autoregressive method;
step S3, the following information is acquired: state transition matrix Fk+1|kObservation matrix HkNew observation sequence yk,yk-1,···,y0Mean value of system noise ukSystem noise variance UkMean value of system observed noise vkSystematic observation of noise variance Rk；
Step S4, Fk+1|k，Hk，{yk,yk-1,···,y0}，uk，Uk，vk，Rk，P0|0，x0|0α, substituting the updated prediction equation to calculate yk+1A value of (d);
step S5, deposit forecast data Sk+1＝yk+1|k；
Step S6, output: deposit prediction data s at time kk+1。
According to an embodiment of the present invention, there is also provided a deposit estimation apparatus for implementing the deposit estimation method, and fig. 4 is a block diagram of a structure of the deposit estimation apparatus according to an embodiment of the present invention, as shown in fig. 4, the deposit estimation apparatus includes: an acquisition module 42, a generation module 44, a conversion module 46 and an estimation module 48, which are described below.
The obtaining module 42 is configured to obtain historical data of the deposit, where the historical data is a time sequence of the deposit in consecutive time periods;
the generation module 44 is used for estimating parameters in the deposit autoregressive model according to the historical data of the deposit and generating the deposit autoregressive model;
the conversion module 46 is configured to convert the deposit autoregressive model to obtain a deposit state space model;
an estimating module 48, configured to estimate deposit data at a predetermined time based on the historical data of the deposit and the deposit state space model, where the predetermined time is a time after the continuous time period.
It should be noted here that the acquiring module 42, the generating module 44, the converting module 46 and the estimating module 48 correspond to steps S202 to S208 in the embodiment, and a plurality of modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in the embodiment. It should be noted that the above modules as a part of the apparatus may be operated in the computer terminal 10 provided in the embodiment.
An embodiment of the present invention may provide a computer device, and optionally, in this embodiment, the computer device may be located in at least one network device of a plurality of network devices of a computer network. The computer device includes a memory and a processor.
The memory may be used to store software programs and modules, such as program instructions/modules corresponding to the deposit estimation method and apparatus in the embodiments of the present invention, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implementing the deposit estimation method. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory located remotely from the processor, and these remote memories may be connected to the computer terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring historical data of the deposit, wherein the historical data is a time sequence of the deposit in continuous time periods; estimating parameters in a deposit autoregressive model according to the historical data of deposits to generate the deposit autoregressive model; converting the deposit autoregressive model to obtain a deposit state space model; estimating deposit data at a predetermined time based on the historical data of the deposit and the deposit state space model, wherein the predetermined time is a time after the continuous time period.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a non-volatile storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the nonvolatile storage medium may be configured to store the program code executed by the deposit estimation method provided in embodiment 1.
Optionally, in this embodiment, the nonvolatile storage medium may be located in any one of computer terminals in a computer terminal group in a computer network, or in any one of mobile terminals in a mobile terminal group.
Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring historical data of the deposit, wherein the historical data is a time sequence of the deposit in continuous time periods; estimating parameters in a deposit autoregressive model according to the historical data of deposits to generate the deposit autoregressive model; converting the deposit autoregressive model to obtain a deposit state space model; estimating deposit data at a predetermined time based on the historical data of the deposit and the deposit state space model, wherein the predetermined time is a time after the continuous time period.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a non-volatile memory storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A deposit estimation method, comprising:
acquiring historical data of the deposit, wherein the historical data is a time sequence of the deposit in continuous time periods;
estimating parameters in a deposit autoregressive model according to the historical data of deposits to generate the deposit autoregressive model;
converting the deposit autoregressive model to obtain a deposit state space model;
estimating deposit data at a predetermined time based on the historical data of the deposit and the deposit state space model, wherein the predetermined time is a time after the continuous time period.
2. The method of claim 1, wherein the estimating parameters in a deposit autoregressive model from the historical data of the deposit, generating the deposit autoregressive model, comprises: and estimating parameters in the deposit autoregressive model by adopting a moment estimation method based on the historical data to obtain the deposit autoregressive model.
3. The method of claim 2, wherein the deposit autoregressive model comprises: s(k)+A1s(k-1)+…+Ans(k-n)＝C0e(k)+C1e(k-1)+…+Cne(k-n)Wherein s is(i)Indicating deposit data at time i, e(i)Representing the model error at time i, AiAnd CiA matrix of m x m representing parameters of the deposit autoregressive model, the ith time being within the continuous time period.
4. The method of claim 3, wherein the credit status space model comprises:
determining a spatial state vector of a k-th moment based on deposit data, a system observation matrix and system observation noise of the k-th moment, wherein the k-th moment is within the continuous time period;
and determining the space state vector at the k +1 th moment based on the space state vector at the k th moment, the system transfer matrix and the system interference noise.
5. The method of claim 4,
the space state vector at the k moment is determined based on the deposit data, the system observation matrix and the system observation noise at the k moment and is determined by the following formula:
wherein s is(k)For the deposit data at the k-th time, [ I ]m Om … Om]For the system observation matrix, C0e(k)The noise is observed for the system in question,
a spatial state vector representing the k-th time instant;
the determining the spatial state vector at the k +1 th time based on the spatial state vector at the k th time, the system transfer matrix and the system interference noise includes:
wherein,
representing the system transfer matrix of the system,
representing system interference noise.
6. The method of claim 5, wherein estimating deposit data at a predetermined time based on the historical data for the deposit and the deposit state space model comprises:
generating a deposit estimation algorithm based on the deposit state space model and an energy gain minimum criterion in Krein space estimation;
and determining the deposit data at the preset moment according to the historical data of the deposit and the deposit estimation algorithm.
7. The method of claim 6, wherein the energy gain minimization criterion in the Krein spatial estimation comprises:
wherein sup denotes supremum, x0Representing the initial state of the model, ukRepresenting the model noise at the time of the k-th instant,
x representing the ith time0Estimate value, UiRepresenting the variance, R, of the model noise u at the i-th momentiRepresents the variance of model noise v at the ith time, gamma represents the index to be estimated at the kth time, PiRepresenting the variance of the estimation error at time i.
8. A deposit estimation device, comprising:
the acquisition module is used for acquiring historical data of the deposit, wherein the historical data is a time sequence of the deposit in continuous time periods;
the generating module is used for estimating parameters in a deposit autoregressive model according to the historical data of deposits and generating the deposit autoregressive model;
the conversion module is used for converting the deposit autoregressive model to obtain a deposit state space model;
and the estimation module is used for estimating deposit data at a preset time based on the historical data of the deposit and the deposit state space model, wherein the preset time is a time after the continuous time period.
9. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls a device in which the non-volatile storage medium is located to perform the deposit estimation method of any one of claims 1 to 7.
10. An electronic device comprising a processor configured to execute a program, wherein the program when executed performs the method of any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024104153A1 (en) * | 2022-11-14 | 2024-05-23 | 苏州元脑智能科技有限公司 | Data enhancement method, system and device, and computer-readable storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111242284A (en) * | 2020-01-10 | 2020-06-05 | 支付宝(杭州)信息技术有限公司 | Prediction method and device |
| CN111882423A (en) * | 2020-07-20 | 2020-11-03 | 中国工商银行股份有限公司 | Deposit interest rate information pushing method and device |
| CN112926803A (en) * | 2021-03-31 | 2021-06-08 | 中国工商银行股份有限公司 | Client deposit loss condition prediction method and device based on LSTM network |
| CN113487110A (en) * | 2021-07-28 | 2021-10-08 | 中国银行股份有限公司 | Spare payment management method and device |
-
2022
- 2022-01-13 CN CN202210037563.2A patent/CN114462686A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111242284A (en) * | 2020-01-10 | 2020-06-05 | 支付宝(杭州)信息技术有限公司 | Prediction method and device |
| CN111882423A (en) * | 2020-07-20 | 2020-11-03 | 中国工商银行股份有限公司 | Deposit interest rate information pushing method and device |
| CN112926803A (en) * | 2021-03-31 | 2021-06-08 | 中国工商银行股份有限公司 | Client deposit loss condition prediction method and device based on LSTM network |
| CN113487110A (en) * | 2021-07-28 | 2021-10-08 | 中国银行股份有限公司 | Spare payment management method and device |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024104153A1 (en) * | 2022-11-14 | 2024-05-23 | 苏州元脑智能科技有限公司 | Data enhancement method, system and device, and computer-readable storage medium |
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