A chaotic time series combined prediction model for improving trend lagging

• Published in: IET communications Vol. 18; no. 12; pp. 701 - 712
• Main Authors: Liu, Fang, Zheng, Yuanfang, Chen, Lizhi, Feng, Yongxin
• Format: Journal Article
• Language: English
• Published: Wiley 01.07.2024
• Subjects: chaos; chaotic time series prediction; combined prediction model; directional trend; dual‐stage attention mechanism; prediction theory; time series
• ISSN: 1751-8628; 1751-8636
• DOI: 10.1049/cmu2.12783

Chaotic time series prediction is a prediction method based on chaos theory, and has important theoretical and application value. At present, most prediction methods only pursue digital fitting and do not consider the directional trend. In addition, using the single model will not achieve better prediction results. Therefore, a chaotic time series combined prediction model for improving trend lagging (ITL) is proposed. An improved dual‐stage attention‐based long short‐term memory model with the improved training objective fuction is designed to solve the trend lagging problem. Then, an auto regressive moving average model with the sliding window is established to mine other characteristics of the time series except nonlinear characteristic. Finally, the idea of optimization algorithm is introduced to construct a time series combined prediction model with high accuracy based on the above two models, so as to perform the chaotic time series prediction from multiple perspectives. Multiple datasets are selected as experimental datasets, and the proposed method is compared with common prediction methods. The results show that the proposed method can achieve single‐step prediction with high accuracy and effectively improve the lagging of chaotic time series prediction. This research can provide theoretical support for the complex chaotic time series prediction. In this paper, a time series combined prediction model for improving trend lagging is proposed. The improved dual‐stage attention‐based long short‐term memory model is designed. And the optimized training objective function is constructed to solve the problem that the prediction methods do not consider the directional trend. The idea of optimization algorithm is introduced to construct a time series combined prediction model with high accuracy, and the time series prediction is performed from multiple perspectives, so as to improve the generalization ability of the model.