Work

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

Year: 2026

Type: article

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.

Source: Intelligent Systems with Applications

DOI: 10.1016/j.iswa.2026.200639

Authors: Ngo Van Son, Vo Viet Minh Nhat

Institutions: Hue University, Hue University of Education

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,790

Funder: Đại học Huế