Exoformer: An improved transformer architecture for long-term time series forecasting based on multi-source data

Abstract

Transformer models have achieved significant success in time series forecasting, but relying solely on endogenous information is insufficient to achieve high accuracy. To enhance predictive performance, some systems have used information from multiple sources, providing additional insights to improve forecasting accuracy. Integrating additional information from external sources involves adding factors not present in the primary source, which affects forecasting results. These additional factors, called exogenous variables, can enhance the forecasting ability. We propose ExoFormer, a Transformer-based architecture that incorporates exogenous information for long-term time series forecasting. By leveraging relative cross-attention and a decoder-only design, ExoFormer efficiently models dependencies between endogenous and exogenous variables. Experiments on seven benchmark datasets demonstrate that ExoFormer consistently outperforms state-of-the-art models in both accuracy and computational efficiency.