WO2019041773A1

WO2019041773A1 - Apparatus and method for updating prediction model, and computer-readable storage medium

Info

Publication number: WO2019041773A1
Application number: PCT/CN2018/077645
Authority: WO
Inventors: 徐亮; 李弦; 商瑾; 肖京
Original assignee: 平安科技（深圳）有限公司
Priority date: 2017-08-29
Filing date: 2018-02-28
Publication date: 2019-03-07
Also published as: CN108427698A

Abstract

Disclosed is an apparatus for updating a prediction model, the apparatus comprising: a memory and a processor, wherein the memory stores a program for updating a prediction model and capable of running on the processor. The following steps are implemented when the program is executed by the processor: determining a time unit to which a current prediction time belongs, and determining a time span set by a user; taking a previous time unit of the time unit as a termination time unit, determining a time interval according to the termination time unit and the time span, and acquiring, from a database, historical data matching the time interval; and inputting the acquired historical data, which acts as a training sample, into a prediction model of an item to be tested for training so as to determine a model parameter of the prediction model, and updating the prediction model on the basis of the determined model parameter. The present application also puts forward a method for updating a prediction model, and a computer-readable storage medium. The present application improves the prediction accuracy of the prediction model.

Description

Update device, method and computer readable storage medium for predictive model

The present application is based on the priority of the Chinese Patent Application entitled "Update Device, Method and Computer-Readable Storage Medium for Predictive Model", filed on August 29, 2017, with the application number of 201710754335.6, which is filed on August 29, 2017. The entire content is incorporated herein by reference.

Technical field

The present application relates to the field of terminal technologies, and in particular, to an apparatus, method, and computer readable storage medium for updating a prediction model.

Background technique

In existing data statistics applications, predictive models are often used, for example, to predict the percentage of influenza-like cases in a region in the future, and to predict the click-through rate of an ad in the future. The traditional method is to use the historical data to establish a prediction model as a fixed prediction model to predict the performance of the next period of time. However, as time progresses, the prediction accuracy of the prediction model will gradually decrease.

Summary of the invention

The present application provides an apparatus, method, and computer readable storage medium for predicting a model, the main purpose of which is to improve the prediction accuracy of the prediction model.

To achieve the above object, the present application provides an apparatus for updating a prediction model, the apparatus comprising: a memory, a processor, and an update program of a prediction model stored on the memory and operable on the processor, the prediction The update procedure of the model is implemented by the processor to implement the following steps:

Determining the time unit to which the current predicted time belongs, and determining the time span set by the user;

Taking a previous time unit of the time unit as an end time unit, determining a time interval according to the end time unit and the time span, and acquiring historical data matching the time interval from a database corresponding to the item to be tested ;

The acquired historical data is input as a training sample into a prediction model of the item to be tested for training to determine model parameters of the prediction model, and the prediction model is updated based on the determined model parameters.

In addition, to achieve the above object, the present application further provides a method for updating a prediction model, the method comprising:

In addition, in order to achieve the above object, the present application further provides a computer readable storage medium, where the update program of a prediction model is stored, and the update program of the prediction model can be executed by at least one processor. To implement the steps of the update method of the predictive model as described above.

The updating device, the method and the computer readable storage medium of the prediction model proposed by the present application determine the time unit to which the current time belongs when receiving the update request triggered by the item to be tested, and determine the time span set by the user, and use the time unit as Terminating the time unit, determining the time interval according to the termination time unit and the time span, obtaining historical data matching the time interval from the database corresponding to the item to be tested, and inputting the acquired historical data as a training sample into the prediction model of the item to be tested. The training is performed to determine the model parameters of the prediction model, and the prediction model is updated based on the determined model parameters. The scheme filters the historical data by setting the time span, and obtains the historical data closest to the target time unit as the training sample. Retraining, and then re-determining the model parameters to achieve the update of the model to adapt to the advancement of time, data changes, and improve the prediction accuracy of the prediction model.

DRAWINGS

1 is a schematic diagram of a preferred embodiment of an apparatus for updating a prediction model of the present application;

2 is a schematic diagram of functional modules of an update procedure of a prediction model in an embodiment of an update apparatus for a prediction model of the present application;

FIG. 3 is a flowchart of a first embodiment of a method for updating a prediction model of the present application.

The implementation, functional features, and advantages of the present application will be further described with reference to the accompanying drawings.

Detailed ways

It is understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting.

The application provides an update device for a predictive model. Referring to FIG. 1, a schematic diagram of a preferred embodiment of an apparatus for updating a prediction model of the present application is shown.

In this embodiment, the update device of the predictive model may be a PC (Personal Computer), or may be a terminal device such as a smart phone, a tablet computer, or a portable computer.

The updating device of the predictive model includes a memory 11, a processor 12, a communication bus 13, and a network interface 14.

The memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 11 may in some embodiments be an internal storage unit of the update device of the predictive model, such as the hard disk of the update device of the predictive model. The memory 11 may also be an external storage device of the update device of the predictive model in other embodiments, such as a plug-in hard disk equipped with an update device of the predictive model, a smart memory card (SMC), and a secure digital (Secure) Digital, SD) cards, flash cards, etc. Further, the memory 11 may also include an internal storage unit of the update device including the prediction model and an external storage device. The memory 11 can be used not only for storing application software of the update device installed in the prediction model and various types of data, such as codes of the update program of the prediction model, but also for temporarily storing data that has been output or is to be output.

The processor 12, in some embodiments, may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor or other data processing chip for running program code or processing stored in the memory 11. Data, such as an update program that performs a predictive model, and the like.

Communication bus 13 is used to implement connection communication between these components.

The network interface 14 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and is typically used to establish a communication connection between the device and other electronic devices.

Figure 1 shows only the updating means of the predictive model with the components 11-14 and the update program of the predictive model, but it should be understood that not all of the illustrated components are required to be implemented, and that more or less can be implemented instead. Component.

Optionally, the device may further include a user interface, the user interface may include a display, an input unit such as a keyboard, and the optional user interface may further include a standard wired interface and a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch sensor, or the like. The display may also be suitably referred to as a display screen or display unit for displaying information processed in the update device of the predictive model and a user interface for displaying the visualization.

Optionally, the device may also include a touch sensor. The area provided by the touch sensor for the user to perform a touch operation is referred to as a touch area. Further, the touch sensor described herein may be a resistive touch sensor, a capacitive touch sensor, or the like. Moreover, the touch sensor includes not only a contact type touch sensor but also a proximity type touch sensor or the like. Furthermore, the touch sensor may be a single sensor or a plurality of sensors arranged, for example, in an array. The area of the display of the device may be the same as or different from the area of the touch sensor. Optionally, a display is stacked with the touch sensor to form a touch display. The device detects a user-triggered touch operation based on a touch screen display.

Optionally, the device may further include a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and the like. Among them, sensors such as light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein if the device is a mobile terminal, the ambient light sensor may adjust the brightness of the display screen according to the brightness of the ambient light, and the proximity sensor may move when the mobile terminal moves to the ear. , turn off the display and / or backlight. As a kind of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (usually three axes), and can detect the magnitude and direction of gravity when stationary, and can be used to identify the posture of the mobile terminal (such as horizontal and vertical screen switching, Related games, magnetometer attitude calibration), vibration recognition related functions (such as pedometer, tapping), etc.; of course, the mobile terminal can also be equipped with other sensors such as gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc. No longer.

In the apparatus embodiment shown in FIG. 1, an update program of a prediction model is stored in the memory 11; when the processor 12 executes an update program of the prediction model stored in the memory 11, the following steps are implemented:

Determine the time unit to which the current forecast time belongs and determine the time span set by the user.

In the solution proposed by the embodiment, when the user needs to update the prediction model of the item to be tested, the user terminal may send an update request, or trigger an update request based on the interaction interface provided by the device, and the system receives the update request or detects When the update request is reached, the above steps are started. Or when the user needs to use the prediction model to predict the situation of a certain time unit in the future, the prediction request may be sent by the user terminal, or the prediction request may be triggered based on the interaction interface provided by the device, when the device receives or detects the prediction request. , start the above steps, update the prediction model and adopt the updated prediction model to predict based on the prediction request. In this embodiment, the continuous time is divided into a plurality of consecutive time units, that is, a plurality of consecutive time units constitute a time axis corresponding to the historical data. Historical data is updated in units of time units in the time dimension. In the following, the scheme is described by taking the week as a time unit. In other embodiments, the time period corresponding to other time units may also be used as a time unit, such as day, month, year, and the like.

For example, the project to be tested is to predict the percentage of influenza-like cases in a certain area in the next week. The percentage of influenza-like cases in a certain area is the total number of influenza-like cases in the sentinel hospitals in the area. The proportion of people. Assuming that the predictive model is a time series model, the database stores historical data as a percentage of the proportion of influenza samples. In other embodiments, the prediction model may also be a regression model or a classification model, such as a random forest model. For a regression model or a classification model, the historical data stored in the database of the project to be tested may be a percentage of the proportion of the influenza sample. Historical data can also be sample data obtained by preprocessing these data.

Simply put, a prediction model can be understood as a function y = f (x ₁ , x ₂ , x ₃ ... x _n ), which reflects the output y and the input characteristics (x ₁ , x ₂ , x ₃ ... The relationship between x _n ). The process of training a model using training samples is a process of obtaining parameters in the function through machine learning, that is, a process of obtaining parameters, and the obtained parameters are the model parameters of the prediction model, and the prediction is determined through training. After the model parameters of the model, the model can be used to predict future conditions.

Taking a previous time unit of the time unit as an end time unit, determining a time interval according to the end time unit and the time span, and acquiring historical data matching the time interval from a database corresponding to the item to be tested .

In this embodiment, in order to realize that the latest historical data can be collected as a training sample during training, the user sets a time span as a screening basis for the historical data, in order to ensure that the model can be performed based on the user's update request at any time. Retraining, and when the model is trained, the latest historical data can be obtained from the database, and the data in the database needs to be updated periodically or periodically. Or, when the database update is not timely, the system prompts the user to update the database when the latest time-interval-compliant data is not obtained from the database.

The current time unit is determined according to the update request, and the time unit is the target time unit to be predicted, and the last time unit of the time unit is used as the termination time unit. The time span set by the user is then determined, and the above termination time unit is the termination time unit of the time span. There are various ways to set the time span, and two of them are described below. In an embodiment, the user may carry the time span change information in the sent update request, and the device detects, when the received update request, whether the update request includes the time span change information; if yes, according to the The time span change information modifies the time span of the current setting. If the time span change information is not included in the update request, the current default time span of the system may be obtained; or, in other embodiments, when the time span is not obtained from the update request, the time span setting interface is displayed, manually Settings.

As another implementation manner, the step of determining a time span set by the user includes:

When the time span change instruction is detected, displaying a time span setting interface for the user to modify the currently set time span based on the setting interface;

The time span modified by the user based on the setting interface is taken as the time span set by the user.

In this embodiment, the user may trigger a time span change instruction based on the interaction interface provided by the system, and when the device detects the time span change instruction, the device displays a time span setting interface, and uses the modified time span of the user as the interface. The time span set by the user.

After determining the time span, the time interval is determined according to the time span and the termination time unit,

Obtain historical data matching the time interval from the database of influenza-like cases, assuming that the database stores 156 weeks of historical data from week 1 of 2014 to week 52 of 2016, with a time span of 100 weeks, assuming users send updates. The time unit at the time of the request is the 20th week of 2017, which means that the percentage of influenza-like cases in the 20th week of 2017 is predicted, and the 19th week of 2017 is the termination time unit. The time interval is determined from the 24th week of 2015 to the 19th week of 2017 based on the time span and the termination time unit. The historical data belonging to the time phase is searched from the database as a training sample input into the prediction model for training to determine the model parameters.

Optionally, as an implementation manner, the processor 12 is further configured to: perform an update procedure of the prediction model, and after obtaining the historical data matching the time interval from the database corresponding to the to-be-tested item, further implement The following steps:

Determining a time interval of missing historical data when no historical data corresponding to the time span corresponding to the time span and adjacent to the target time unit is acquired from the database;

Generating data supplemental prompt information based on the determined time interval for the user to update the database based on the prompt information to supplement the missing historical data;

And the step of acquiring the historical data matching the time interval from the database corresponding to the item to be tested is re-executed based on the updated database.

In the above historical data acquisition process, when the influenza-like case data in the database is not updated in time, the historical data of some time units may not be obtained. For example, in the above example, the time interval is from the 24th week of 2015 to the 19th week of 2017, but the database stores historical data of 156 weeks from the first week of 2014 to the 52nd week of 2016. Therefore, the data in the database needs to be updated. At this time, it is determined that the time interval of the missing historical data is from the first week of 2017 to the 19th week of 2017, and the data supplemental prompt information is generated based on the time interval, and the user pairs the information according to the information. The missing historical data in the database is supplemented, and the database is updated based on the supplemental historical data, and the historical data matching the time interval is re-discovered.

After finding the historical data, it is input as a training sample into the prediction model for training, such as a time series model, using the data from the first week to the 100th week as the training sample training prediction model, and the incidence of the influenza-like case on the 101st week. Rate to make predictions. During training, the model parameters are learned according to the algorithm corresponding to the model, and the prediction model that determines the model parameters is updated to the latest prediction model. According to the above scheme, when the incidence of influenza-like cases in the 102nd week is predicted, the data from the 2nd week to the 101st week is used as the training sample training prediction model, and so on, the prediction model is continuously updated over time.

Further, in some embodiments, in order to improve the modeling efficiency, before the step of training the acquired historical data as a training sample into the prediction model of the test item, the current prediction model pair is used to terminate. The percentage of influenza-like cases in the time unit is predicted, and it is judged whether the difference between the predicted value and the actual observation value is within the allowable error range. If so, the prediction accuracy of the current prediction model is considered to be in compliance with the requirement, and the prediction model is not performed. Update, if the difference exceeds the allowable error range, re-train the prediction model.

The updating device of the prediction model proposed by the embodiment determines the target time unit to be predicted according to the received update request triggered by the item to be tested, and determines the time span set by the user, and searches for the matching time from the database corresponding to the item to be tested. The historical data corresponding to the time unit adjacent to the target time unit, the acquired historical data is input as a training sample into the prediction model of the test item to be trained to determine the model parameter of the prediction model, based on the determined model parameter pair The prediction model is updated. The scheme filters the historical data by setting the time span, and obtains the historical data closest to the target time unit as the training sample to retrain the model, and then re-determine the model parameters to realize the update of the model. Adaptation time advancement and data changes improve the prediction accuracy of the prediction model.

Optionally, in other embodiments, the update program of the predictive model may also be divided into one or more modules, one or more modules are stored in the memory 11 and executed by one or more processors (this implementation) For example, the processor 12) is executed to complete the application, and a module referred to herein refers to a series of computer program instructions that are capable of performing a particular function. For example, referring to FIG. 2, it is a functional block diagram of an update procedure of a prediction model in an embodiment of an update apparatus of a prediction model of the present application. In this embodiment, an update procedure of the prediction model may be divided into a determination module 10 and acquired. Module 20 and training module 30, by way of example:

The determining module 10 is configured to determine a time unit to which the current predicted time belongs, and determine a time span set by the user;

The obtaining module 20 is configured to use the last time unit of the time unit as an end time unit, determine a time interval according to the end time unit and the time span, and obtain the time from the database corresponding to the item to be tested. Historical data of interval matching;

The training module 30 is configured to input the acquired historical data as a training sample into a prediction model of the item to be tested for training, determine model parameters of the prediction model, and update the prediction model based on the determined model parameters.

The functions or operation steps performed by the above-mentioned determination module 10, the acquisition module 20, and the training module 30 are substantially the same as those of the foregoing embodiment, and are not described herein again.

In addition, the present application also provides an update method of a prediction model. Referring to FIG. 3, it is a flowchart of the first embodiment of the method for updating the prediction model of the present application. The method can be performed by a device that can be implemented by software and/or hardware.

In this embodiment, the update method of the prediction model includes:

In step S10, the time unit to which the current predicted time belongs is determined, and the time span set by the user is determined.

In the solution proposed by the embodiment, when the user needs to update the prediction model of the item to be tested, the user terminal may send an update request, or trigger an update request based on the interaction interface provided by the device, and the system receives the update request or detects When the update request is made, a step of determining the time unit to which the current predicted time belongs is performed. Or when the user needs to use the prediction model to predict the situation of a certain time unit in the future, the prediction request may be sent by the user terminal, or the prediction request may be triggered based on the interaction interface provided by the device, when the system receives or detects the prediction request, Begin the above steps, update the prediction model and adopt the updated prediction model to predict based on the prediction request. In this embodiment, the continuous time is divided into a plurality of consecutive time units, that is, a plurality of consecutive time units constitute a time axis corresponding to the historical data. Historical data is updated in units of time units in the time dimension. In the following, the scheme is described by taking the week as a time unit. In other embodiments, the time period corresponding to other time units may also be used as a time unit, such as day, month, year, and the like.

In step S20, the last time unit of the time unit is used as the termination time unit, and the time interval is determined according to the termination time unit and the time span, and the time interval is matched from the database corresponding to the item to be tested. Historical data.

In this embodiment, in order to be able to collect the latest historical data as a training sample during training, the user sets a time span as a screening basis for historical data, in order to ensure that the model can be performed based on the user's update request at any time. Retraining, and the latest historical data can be obtained from the database during model training, and the data in the database needs to be updated periodically or periodically. Or, when the database update is not timely, the system prompts the user to update the database when the latest time-interval-compliant data is not obtained from the database.

Determining a current time unit according to the update request, the time unit being the target time unit to be predicted, using the last time unit of the time unit as the termination time unit, and then determining the time span set by the user, where the termination time unit is the time The termination time unit of the span. There are various ways to set the time span, and two of them are described below. In an embodiment, the user may carry time span change information in the sent update request, and the method detects, when the received update request, whether the update request includes time span change information; if yes, according to the The time span change information modifies the time span of the current setting. If the time span change information is not included in the update request, the current default time span of the system may be obtained; or, in other embodiments, when the time span is not obtained from the update request, the time span setting interface is displayed, manually Settings.

In this embodiment, the user may trigger a time span change instruction based on the interaction interface provided by the system, and when the time span change instruction is detected, the method displays a time span setting interface, and uses the modified time span of the user as the interface. The time span set by the user.

Step S30: The acquired historical data is input as a training sample into the prediction model of the item to be tested for training to determine model parameters of the prediction model, and the prediction model is updated based on the determined model parameters.

After determining the time span, the time interval is determined according to the time span and the termination time unit, and historical data matching the time interval is obtained from the database of influenza-like cases, assuming that the database stores the week from the first week of 2014 to the 52nd week of 2016. A total of 156 weeks of historical data, the time span is 100 weeks, assuming that the time unit when the user sends the update request is the 20th week of 2017, that is, the forecast of the percentage of influenza-like cases in the 20th week of 2017, 2017 The 19th week of the year is the termination time unit. The time interval is determined from the 24th week of 2015 to the 19th week of 2017 based on the time span and the termination time unit. The historical data belonging to the time phase is searched from the database as a training sample input into the prediction model for training to determine the model parameters.

Optionally, as an implementation manner, after step S20, the method further includes the following steps:

The method for updating the prediction model proposed in this embodiment determines the target time unit to be predicted according to the received update request triggered by the item to be tested, and determines the time span set by the user, and searches for the matching time from the database corresponding to the item to be tested. The historical data corresponding to the time unit adjacent to the target time unit, the acquired historical data is input as a training sample into the prediction model of the test item to be trained to determine the model parameter of the prediction model, based on the determined model parameter pair The prediction model is updated. The scheme filters the historical data by setting the time span, and obtains the historical data closest to the target time unit as the training sample to retrain the model, and then re-determine the model parameters to realize the update of the model. Adaptation time advancement and data changes improve the prediction accuracy of the prediction model.

In addition, the embodiment of the present application further provides a computer readable storage medium, where the update program of the prediction model is stored, and the update program of the prediction model may be executed by at least one processor to implement the following operating:

Further, when the update program of the prediction model is executed by the processor, the following operations are also implemented:

Detecting whether the update request includes time span change information;

If yes, modify the time span of the current setting according to the time span change information.

The specific embodiment of the computer readable storage medium of the present application is substantially the same as the embodiment of the foregoing apparatus and method for updating the prediction model, and is not described herein.

It should be noted that the foregoing serial numbers of the embodiments of the present application are merely for the description, and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising", or any other variations thereof are intended to encompass a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a plurality of elements includes not only those elements but also Other elements listed, or elements that are inherent to such a process, device, item, or method. An element that is defined by the phrase "comprising a ..." does not exclude the presence of additional equivalent elements in the process, the device, the item, or the method that comprises the element.

Through the description of the above embodiments, those skilled in the art can clearly understand that the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better. Implementation. Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM as described above). , a disk, an optical disk, including a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods described in the various embodiments of the present application.

The above is only a preferred embodiment of the present application, and is not intended to limit the scope of the patent application, and the equivalent structure or equivalent process transformations made by the specification and the drawings of the present application, or directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of this application.

Claims

An apparatus for updating a prediction model, the apparatus comprising: a memory, a processor, an update program of a prediction model executable on the processor, and an update program of the prediction model The following steps are implemented when executed by the processor:

Determining the time unit to which the current predicted time belongs, and determining the time span set by the user;

Taking a previous time unit of the time unit as an end time unit, determining a time interval according to the end time unit and the time span, and acquiring historical data matching the time interval from a database corresponding to the item to be tested ;

The acquired historical data is input as a training sample into a prediction model of the item to be tested for training to determine model parameters of the prediction model, and the prediction model is updated based on the determined model parameters.
The apparatus for updating a prediction model according to claim 1, wherein the processor is further configured to execute an update program of the prediction model to acquire the time from a database corresponding to the item to be tested After the step of matching the historical data of the interval, the following steps are also implemented:

Determining a time interval of missing historical data when no historical data corresponding to the time span corresponding to the time span and adjacent to the target time unit is acquired from the database;

Generating data supplemental prompt information based on the determined time interval for the user to update the database based on the prompt information to supplement the missing historical data;

And the step of acquiring the historical data matching the time interval from the database corresponding to the item to be tested is re-executed based on the updated database.
The apparatus for updating a prediction model according to claim 1, wherein the step of determining a time span set by the user comprises:

Detecting whether the update request includes time span change information;

If yes, modify the time span of the current setting according to the time span change information.
The apparatus for updating a prediction model according to claim 2, wherein the step of determining a time span set by the user comprises:

Detecting whether the update request includes time span change information;

If yes, modify the time span of the current setting according to the time span change information.
The apparatus for updating a prediction model according to claim 1, wherein the step of determining a time span set by the user comprises:

When the time span change instruction is detected, displaying a time span setting interface for the user to modify the currently set time span based on the setting interface;

The time span modified by the user based on the setting interface is taken as the time span set by the user.
The apparatus for updating a prediction model according to claim 2, wherein the step of determining a time span set by the user comprises:

When the time span change instruction is detected, displaying a time span setting interface for the user to modify the currently set time span based on the setting interface;

The time span modified by the user based on the setting interface is taken as the time span set by the user.
The apparatus for updating a prediction model according to claim 3, wherein the prediction model is a time series model, a classification model, or a regression model.
A method for updating a prediction model, the method comprising:

Determining the time unit to which the current predicted time belongs, and determining the time span set by the user;

Taking a previous time unit of the time unit as an end time unit, determining a time interval according to the end time unit and the time span, and acquiring historical data matching the time interval from a database corresponding to the item to be tested ;

The acquired historical data is input as a training sample into a prediction model of the item to be tested for training to determine model parameters of the prediction model, and the prediction model is updated based on the determined model parameters.
The method for updating a prediction model according to claim 8, wherein after the step of acquiring historical data matching the time interval from the database corresponding to the item to be tested, the method further comprises the steps of:

Determining a time interval of missing historical data when no historical data corresponding to the time span corresponding to the time span and adjacent to the target time unit is acquired from the database;

Generating data supplemental prompt information based on the determined time interval for the user to update the database based on the prompt information to supplement the missing historical data;

And the step of acquiring the historical data matching the time interval from the database corresponding to the item to be tested is re-executed based on the updated database.
The method for updating a prediction model according to claim 8, wherein the step of determining a time span set by the user comprises:

Detecting whether the update request includes time span change information;

If yes, modify the time span of the current setting according to the time span change information.
The method for updating a prediction model according to claim 9, wherein the step of determining a time span set by the user comprises:

Detecting whether the update request includes time span change information;

If yes, modify the time span of the current setting according to the time span change information.
The method for updating a prediction model according to claim 8, wherein the step of determining a time span set by the user comprises:

When the time span change instruction is detected, displaying a time span setting interface for the user to modify the currently set time span based on the setting interface;

The time span modified by the user based on the setting interface is taken as the time span set by the user.
The method for updating a prediction model according to claim 9, wherein the step of determining a time span set by the user comprises:

When the time span change instruction is detected, displaying a time span setting interface for the user to modify the currently set time span based on the setting interface;

The time span modified by the user based on the setting interface is taken as the time span set by the user.
The method for updating a prediction model according to claim 10, wherein the prediction model is a time series model, a classification model, or a regression model.
A computer readable storage medium, wherein the computer readable storage medium stores an update program of a predictive model, and the update program of the predictive model is executable by at least one processor to implement the following steps:

Determining the time unit to which the current predicted time belongs, and determining the time span set by the user;

Taking a previous time unit of the time unit as an end time unit, determining a time interval according to the end time unit and the time span, and acquiring historical data matching the time interval from a database corresponding to the item to be tested ;

The acquired historical data is input as a training sample into a prediction model of the item to be tested for training to determine model parameters of the prediction model, and the prediction model is updated based on the determined model parameters.
The computer readable storage medium according to claim 15, wherein the update program of the prediction model is executable by at least one processor to acquire the time interval from a database corresponding to the item to be tested After the steps of matching historical data, the following steps are also implemented:

Determining a time interval of missing historical data when no historical data corresponding to the time span corresponding to the time span and adjacent to the target time unit is acquired from the database;

Generating data supplemental prompt information based on the determined time interval for the user to update the database based on the prompt information to supplement the missing historical data;

And the step of acquiring the historical data matching the time interval from the database corresponding to the item to be tested is re-executed based on the updated database.
The computer readable storage medium of claim 15, wherein the step of determining a time span set by a user comprises:

Detecting whether the update request includes time span change information;

If yes, modify the time span of the current setting according to the time span change information.
The computer readable storage medium of claim 16, wherein the step of determining a time span set by a user comprises:

Detecting whether the update request includes time span change information;

If yes, modify the time span of the current setting according to the time span change information.
The computer readable storage medium of claim 15, wherein the step of determining a time span set by a user comprises:

When the time span change instruction is detected, displaying a time span setting interface for the user to modify the currently set time span based on the setting interface;

The time span modified by the user based on the setting interface is taken as the time span set by the user.
The computer readable storage medium of claim 16, wherein the step of determining a time span set by a user comprises:

When the time span change instruction is detected, displaying a time span setting interface for the user to modify the currently set time span based on the setting interface;

The time span modified by the user based on the setting interface is taken as the time span set by the user.