Wang Weina, Shao Jiapeng, Jumahong Huxidan
College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132022, China.
School of Network Security and Information technology, YiLi Normal University, Yining, 835000, China.
Sci Rep. 2023 Nov 21;13(1):20359. doi: 10.1038/s41598-023-47812-3.
Long short-term memory (LSTM) based time series forecasting methods suffer from multiple limitations, such as accumulated error, diminishing temporal correlation, and lacking interpretability, which compromises the prediction performance. To overcome these shortcomings, a fuzzy inference-based LSTM with the embedding of a fuzzy system is proposed to enhance the accuracy and interpretability of LSTM for long-term time series prediction. Firstly, a fast and complete fuzzy rule construction method based on Wang-Mendel (WM) is proposed, which can enhance the computational efficiency and completeness of the WM model by fuzzy rules simplification and complement strategies. Then, the fuzzy prediction model is constructed to capture the fuzzy logic in data. Finally, the fuzzy inference-based LSTM is proposed by integrating the fuzzy prediction fusion, the strengthening memory layer, and the parameter segmentation sharing strategy into the LSTM network. Fuzzy prediction fusion increases the network reasoning capability and interpretability, the strengthening memory layer strengthens the long-term memory and alleviates the gradient dispersion problem, and the parameter segmentation sharing strategy balances processing efficiency and architecture discrimination. Experiments on publicly available time series demonstrate that the proposed method can achieve better performance than existing models for long-term time series prediction.
基于长短期记忆（LSTM）的时间序列预测方法存在多种局限性，如累积误差、时间相关性递减和缺乏可解释性，这些都会损害预测性能。为了克服这些缺点，提出了一种嵌入模糊系统的基于模糊推理的LSTM，以提高LSTM在长期时间序列预测中的准确性和可解释性。首先，提出了一种基于王-门德尔（WM）的快速完整模糊规则构建方法，该方法可通过模糊规则简化和补充策略提高WM模型的计算效率和完整性。然后，构建模糊预测模型以捕捉数据中的模糊逻辑。最后，通过将模糊预测融合、强化记忆层和参数分割共享策略集成到LSTM网络中，提出了基于模糊推理的LSTM。模糊预测融合提高了网络推理能力和可解释性，强化记忆层增强了长期记忆并缓解了梯度弥散问题，参数分割共享策略平衡了处理效率和架构区分度。在公开可用时间序列上的实验表明，所提出的方法在长期时间序列预测中比现有模型能取得更好的性能。
