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Echo State Network based on Phase Space Reconstruction:El Ni?o 3 Index Forecasting

Shaoshuai Wang  Jiezhou Yu  Jun Meng  
【摘要】:Climate forecasting,especially for the abnormal weather like El Ni?o and La Ni?a etc.,is one of the most difficult tasks of time series prediction domain,because of its inherent difficulty of long-term forecasting.In this paper,we investigate whether there is a simple pattern to describe El Ni?o phenomenon that can be reconstructed by mathematical models and a multiple steps ahead predicting method is proposed,which is an improved echo state network(ESN) using the phase-space reconstruction(PSR).PSR is to embed a one-dimensional time series sequence in a high-dimension phase space as the input of the ESN.Specifically,the method PSR-ESN can capture seasonal characteristics of the climate data and reduce the lag of prediction as much as possible.The method is evaluated on a real Ni?o 3 SST index data and it outperforms the other state-of-the-art baselines from the perspective of the magnitude and the lag.

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