Climate time sequence forecasting method based on empirical mode decomposition and support vector machine

Abstract

The invention discloses a climate time sequence forecasting method based on an empirical mode decomposition and support vector machine, belonging to the field of short-term climate forecasting. The climate time sequence forecasting method comprises the following steps of: firstly, pretreating a time sequence through an empirical mode decomposition method; decomposing the time sequence to a plurality of intrinsic mode function components and a trend component, wherein the components can more accurately reflect changes in the original sequence and keep characters of the time sequence per se; next, carrying out phase space reconstruction on each component through a time sequence forecasting method; respectively establishing different support vector machine regression models for forecasting; and combining the forecasted result of each component to the forecasted result of the original sequence. The invention has the advantages of getting help from the empirical mode decomposition method for smooth processing of the time sequence, reducing interference or coupling information among the sequences on the basis of keeping the characters of the time sequence per se, enabling the accuracy of forecasting to be higher, and especially fitting for treating non-stationary climate time sequences with yearly precipitation or changed temperature.

Description

technical field [0001] The invention belongs to the field of short-term climate prediction and relates to a climate time series prediction method based on empirical mode decomposition and support vector machine. The empirical mode decomposition algorithm is used to decompose the climate time series, and then the support vector machine algorithm is used to predict the time series to improve the accuracy of climate time series prediction, which is suitable for dealing with climate time series with annual precipitation or temperature changes. Background technique [0002] The climate system is a dissipative high-order nonlinear system. In the forecasting methods for climate time series, technologies such as artificial neural networks have a stronger ability to deal with nonlinear problems, so they are more efficient than general linear forecasting methods. Good predictive ability has been applied to some extent. For example, Zhang Yingchun [1] et al. used the BP neural networ...

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However, these studies are based on climate analysis through smooth

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