Ablation experiments at PeMSD3 and PeMSD4.

Time series prediction is a widely used key technology, and traffic flow prediction is its typical application scenario. Traditional time series prediction models such as LSTM (Long Short- Term Memory) and CNN (Convolution Neural Network)-based models have limitations in dealing with complex nonlinear time dependencies and are difficult to capture the complex characteristics of traffic flow data. In addition, traditional methods usually rely on manually designed attention mechanisms and are difficult to adaptively focus on key features. To improve the accuracy of time series prediction, the paper proposes a multiscale convolutional attention long short-term memory model (MSCALSTM), which combines a multiscale convolutional neural network (MSCNN), a multiscale convolutional block attention module (MSCBAM) and LSTM. MSCNN can effectively capture multiscale dynamic patterns in time series data, MSCBAM can adaptively focus on key features, and LSTM is good at modeling complex time dependencies. The MSCALSTM model makes full use of the advantages of the above technologies and greatly improves the accuracy and robustness of time series prediction. Extensive experiments are conducted on a dataset from the California Performance Measurement System (PEMS), and the results show that the proposed MSCALSTM model outperforms the state-of-the-art models. Experiments in the Energy domain show that our model also has strong generalization properties in other time series forecasting domains.

时间序列预测是一项应用广泛的关键技术，交通流预测是其典型应用场景。传统时间序列预测模型，如长短期记忆网络（Long Short-Term Memory，LSTM）与基于卷积神经网络（Convolution Neural Network，CNN）的模型，在处理复杂非线性时间依赖关系时存在局限性，且难以捕捉交通流数据的复杂特征。此外，传统方法通常依赖人工设计的注意力机制，无法自适应地聚焦关键特征。
为提升时间序列预测精度，本文提出多尺度卷积注意力长短期记忆模型（multiscale convolutional attention long short-term memory，MSCALSTM），该模型融合了多尺度卷积神经网络（multiscale convolutional neural network，MSCNN）、多尺度卷积块注意力模块（multiscale convolutional block attention module，MSCBAM）与长短期记忆网络（LSTM）。其中，多尺度卷积神经网络可有效捕捉时间序列数据中的多尺度动态模式，多尺度卷积块注意力模块能够自适应聚焦关键特征，而长短期记忆网络则擅长对复杂时间依赖关系进行建模。
该模型充分利用上述各项技术的优势，大幅提升了时间序列预测的精度与鲁棒性。本文在加州性能测量系统（California Performance Measurement System，PEMS）的数据集上开展了大量实验，实验结果表明，所提MSCALSTM模型的性能优于现有最优模型。在能源领域的实验进一步验证，该模型在其他时间序列预测领域同样具备出色的泛化能力。