Time series forecasting method and system based on SVR (Support Vector Regression)

A support vector regression and time series technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as accumulation of prediction errors and reduction of data accuracy

Inactive Publication Date: 2012-02-22
SUZHOU UNIV
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Problems solved by technology

[0005] In view of this, the embodiment of the present application discloses a time series forecasting method and system based on support vector regression to solve the problem of the existing one-step time forecasting method of a single SVR model. The number of predictions increases and accumulates, which directly reduces the accuracy of the predicted data

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  • Time series forecasting method and system based on SVR (Support Vector Regression)
  • Time series forecasting method and system based on SVR (Support Vector Regression)
  • Time series forecasting method and system based on SVR (Support Vector Regression)

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Embodiment Construction

[0037] In order to make the above objects, features and advantages of the present application more obvious and comprehensible, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0038] an embodiment

[0039] see figure 1 , which shows a flow chart of Embodiment 1 of an online service request identification method of the present application, which may include the following steps:

[0040] S101: Select historical data from an existing time series data set to obtain multiple training data sets.

[0041] Wherein, each training data set includes multiple existing subsets of time series data and predicted values ​​corresponding to each subset of time series data, and any subset of time series data and its corresponding predicted value is the time series data Centralized historical data.

[0042] Assuming that the historical data in the time series data set is x(k), k=0, 1, ..., t-1...

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Abstract

The invention discloses a time series forecasting method and system based on SVR (Support Vector Regression). The time series forecasting method based on SVR comprises the following steps of: selecting historical data from an existing time series data set and obtaining a plurality of training data sets; determining regular parameters and Gaussian nuclear parameters of SVR modules to be constructed, and constructing an SVR module corresponding to each training data set; selecting T historical data between a moment of t-T+1 to a current moment t; and under the condition that a first difference of a moment to be forecasted and the current moment is less than or equal to the number of SVR modules, selecting the SVR module corresponding to the first difference, and obtaining a forecast value of the moment to be forecasted from the T historic data by directly utilizing the SVR module. Compared with a method for obtaining the forecast value of the moment to be forecasted through multi-step forecasting in the prior art, in the method for obtaining the forecast value through a one-step forecasting disclosed by the invention, the accumulation of forecast errors is reduced, and thus the accuracy of obtaining the forecast value is improved.

Description

technical field [0001] The present application relates to the field of time series forecasting, in particular to a time series forecasting method and system based on support vector regression. Background technique [0002] Time series is an ordered sequence of observed data, which reflects the time distribution of a certain statistical index of a certain phenomenon, such as the electricity load data of a certain region from October 1 to 15. The time series forecasting method is to use the obtained historical time series collection, analyze the inherent statistical characteristics and development laws of the historical data in the collection, and obtain forecast data to show the development trend of the data. [0003] At present, the commonly used time series prediction method is a time prediction method based on a single SVR (Support Vector Regression, Support Vector Regression). This method first selects historical data from the existing time series data set to obtain a tra...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 张莉周伟达王邦军李凡长杨季文何书萍
Owner SUZHOU UNIV
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