A Time Series Data Prediction Model Based on Adaptive Weighted LSTM

Abstract

Financial time series prediction has always been a hot topic in the field of statistics learning. Aiming at the step selection problem of LSTM time series prediction model, this paper proposes an adaptive weighted LSTM model based on model average method. The model average is mainly reflected in two aspects: On the one hand, the proposed method takes intraday price information into account. Firstly, functional and nonlinear information of intraday price series are extracted through functional principal component analysis and kernel principal component analysis, and then Bagging is used to fit the residual sequence generated by the original LSTM model. On the other hand, the proposed method integrates the information of the model under different time Windows by using the weight based on distance correlation coefficient, and adaptively solves the step size selection problem, so as to improve the effectiveness of the overall model. The actual data analysis results show that the proposed method can effectively improve the prediction accuracy of the original LSTM model and has a certain robustness. Due to the flexibility of the proposed method, it can be used in time series prediction such as energy consumption prediction, environment detection and road traffic flow monitoring.