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Time series forecasting method and equipment and system adopting same

A technology of time series and forecasting methods, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of slow calibration process and unsatisfactory time series forecasting accuracy, and achieve the effect of improving forecasting accuracy.

Inactive Publication Date: 2014-11-05
SUZHOU UNIV
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Problems solved by technology

[0005] Since the output results of multiple predictors are fused, the average weighting method, the least squares weighting method, and the linear programming coefficient weighting method are used. The average weighting method and the least squares weighting method are all predictors. When verifying the predictor data, it is necessary to verify the predictors one by one, which undoubtedly makes the verification process slow. Although the linear programming coefficient weighting method is relatively fast in the verification process, the accuracy of time series prediction is not ideal

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  • Time series forecasting method and equipment and system adopting same
  • Time series forecasting method and equipment and system adopting same
  • Time series forecasting method and equipment and system adopting same

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Embodiment Construction

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0048] The time series forecasting method is a method of analogizing or extending according to the development process, direction and trend reflected in the time series by compiling and analyzing the time series.

[0049] The more commonly used machine learning algorithm tools in the existing time series forecasting method include predictors, and the predictors based on the time series forecasting method are mainly support vector machines, decision trees and neural ...

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Abstract

The embodiment of the invention discloses a time series forecasting method and equipment and a system adopting the same. The method comprises the following steps: training acquired time series data to obtain a training data set; utilizing the training data set to train a selected predictor group to generate a diversified predictor group; extracting sparse signals to restructure majorized functions and solving the weighting factor of the diversified predictor group; and intercepting predictors with non-zero weighting factors to forecast the time series data. In the embodiment of the invention, the weighting factor of the diversified predictor group serves as the sparse signals for restructuring and solving the corresponding majorized functions, and as the obtained weighting factor has sparseness, the obtained weighting factor is utilized to achieve time series data forecasting and checking for the predictors with non-zero weighting factors; and as the quantity of the predictor group is reduced, the checking is sped up and the forecasting accuracy is improved.

Description

technical field [0001] The present invention relates to the technical field of data prediction, and more specifically, to a time series prediction method, device and method. Background technique [0002] The time series forecasting method is based on the ordered observation data sets associated with the time sequence, and uses the stochastic process theory and mathematical statistics methods to study the statistical laws obeyed by the data sets, so as to infer the data development trend and guide the solution of practical problems. The time series prediction method has been widely applied to fields such as industry, address, ecology, economy, meteorology, and medicine. [0003] In the prior art, the most used tools for time series forecasting are predictors such as neural networks, decision trees and support vector machine methods based on machine learning algorithms. In order to achieve better time series forecasting results, multiple identical or Different predictors are ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/00
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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