FCP-Former: Enhancing Long-Term Multivariate Time Series Forecasting with Frequency Compensation

Time series forecasting can be applied to various aspects of daily life, providing valuable insights to inform decision-making in areas such as stock market, electricity load, traffic flow and health-care, among others. Many previous studies have demonstrated the effectiveness of transformer-based models in long-term time series forecasting tasks, and the introduction of patch mechanisms can further enhance the predictive performance of Transformer-based models. However, most of the previous works have focused on reducing the computational resource overhead of patch-wise models, with little consideration given to the potential decrease in the model's ability to capture information from each time step within the patches. This often limits the further improvement of prediction accuracy in patch-wise models. To Address this issue, we propose a frequency compensation block, which incorporates frequency-domain data features into each patch before performing the time series forecasting task, to improve patch-wise model, called Frequency Compensation Patch-wise transFormer(FCP-Former). Experimental results demonstrate that the proposed method achieve better performance compared with the state-of-the-art methods in multivariate time series forecasting tasks.