Time Series Prediction Based On Improved Echo State Network

Time series is a collection of data arranged in chronological order.Time series forecasting methods are widely used in fields such as economics,meteorology,stock market,and traffic forecasting.These forecasts are crucial to assist people in making decisions and planning strategies.In recent years,as a research hotspot in the field of time series prediction,echo state network has attracted extensive attention from scholars at home and abroad.However,this method still needs to be further improved in terms of the training process and structure optimization.Based on echo state network,this paper improves its learning algorithm and network structure through optimization to improve the prediction accuracy.The specific contents include:In response to the issues of structural uncertainty and internal redundancy in the echo state network for time series prediction,an improved cycle reservoir with step jumps network is proposed.The network model adopts the topology structure of one-way ring connection and shares the connection weight,which can effectively avoid the network instability caused by random connections and ensure prediction accuracy.The bidirectional step size jump mode is proposed to reduce the redundancy of internal network connections,reduce the complexity of the reservoir,and effectively improve the speed of network construction.Experimental results show that the proposed algorithm has better performance in single-step prediction of time series.In order to solve the problem that the prediction accuracy of the time series prediction method based on echo state network will be reduced if only a deterministic cyclic jump network is used,a multilevel hierarchical step jumps network is proposed.The multilevel construction of the network is based on the deterministic step-hopping network reservoir,which makes the internal weights of the reservoir more closely connected.When the internal structural dimension of the reservoir is large,the topology structure of the multilevel step hopping cyclic state network shows more accurate prediction performance in time series prediction.The experimental results show that the improved reservoir structure can effectively improve the accuracy and efficiency of complex time series prediction tasks.In view of the large size and complex calculation of the state matrix of the reserve pool in the echo state network model,a locality preserving projections echo state network model was proposed.In this model,the locality preserving projections dimension reduction algorithm is used to extract the low-dimensional manifold features of the state matrix by restoring the nonlinear connection relation of the reservoir nodes of echo state network.Compared with the traditional principal component analysis dimension reduction model,this algorithm can reduce local dimension better for large-scale reservoirs and improve the prediction accuracy of time series.The experimental results show that the dimensionality reduction model has good predictive ability in practical application.