Time Series Completion and One-step Prediction Based on Two-channel Echo State Network
With the development of the Internet of Things,numerous sensors can collect a large number of time series with rich data correlation,providing powerful data support for various data mining applications.However,some objective or subjective rea-sons(such as equipment failure,sparse sensing)often lead to the loss of collected data to varying degrees.Although many approa-ches have been proposed to solve this problem,data correlation is either not fully considered or computationally expensive.In ad-dition,existing methods only focus on the completion of missing values,and fail to take into account downstream applications.Ai-ming at the above shortcomings,this paper designs a two-channel echo state network to achieve both the completion task and the prediction task.Although the two channels share the input layer,they have their own reservoir and output layer.The biggest difference between them is that the output layer of the left/right channels respectively represents the target value or prefilled va-lue corresponding to the moment before/after the input layer.Finally,by fusing the estimates of the two channels,the data corre-lation from before and after the missing moments is fully utilized to further improve performance.Experimental results of diffe-rent missing rates with two missing mechanisms(random missing and piecewise missing)show that the proposed model is superi-or to the current methods in both completion accuracy and prediction accuracy.
Data correlationTime seriesExogenous variablesTwo-channel echo state networkMissing value completionOne-step
万方数据
维普
