Time series prediction method based on the bidirectional long short-term memory network

Time series prediction means the use of historical time series to predict a period of time in the future, so as to formulate corresponding strategies in advance. At present, the categories of time series are complex and diverse. However, existing time series prediction models cannot achieve stable prediction results when faced with multiple types of time series data. The application requirements of complex time series data prediction in reality are difficult to simultaneously meet. To address the problem, a time series prediction method is proposed based on the Bidirectional Long and Short-term Memory ( BLSTM ) with the attention mechanism. The improved forward and backward propagation mechanisms are used to extract temporal information. The future temporal information is inferred through an adaptive weight allocation strategy. Specifically, an improved BLSTM is proposed to extract deep time series features and explore temporal dependencies of context by combining BLSTM and Long Short-term Memory (LSTM) networks, on the basis of which the proposed temporal attention mechanism is fused to achieve adaptive weighting of deep time series features, which improves the saliency expression ability of deep time series features. Experimental results demonstrate that the proposed method has a superior prediction performance in comparison with some representative methods in multiple time series dataseis of different categories. © 2024 ournal of Xidian University. All Rights Reserved.