MTS-CAM:A Multi-Time-Scale Convolutional Attention Mechanism for Time Series Forecasting

Recent research in time series forecasting has demonstrated significant advancements using models based on the Transformer architecture. However, most existing studies treat each variable as an independent channel, which fails to effectively capture the dynamic dependencies among multiple variables. This limitation impedes the model's ability to understand complex dynamic relationships in real-world data, thereby reducing predictive accuracy. To address this issue, we propose a novel framework called MTS-CAM (Multi-Time-Scale Convolutional Attention Mechanism). MTS-CAM captures various cycles and trends across different time scales and employs a convolutional attention module to identify key features, while dynamically adjusting the importance of each channel and spatial position within the feature map. This comprehensive approach enables the model to account for dynamic relationships and feature variations among variables. Experimental results on six benchmark time series forecasting datasets validate the effectiveness of the proposed model, showing that it significantly outperforms existing methods and achieves state-of-the-art performance.