Ablation experiments on four datasets.

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.