TimeXer introduces a unique approach to time series forecasting by incorporating exogenous variables, improving accuracy and interpretability. The model effectively captures temporal dependencies and multivariate correlations, outperforming traditional methods. By empowering Transformers with external information, TimeXer demonstrates its potential in complex real-world scenarios.
Recent studies have shown significant advancements in time series forecasting, emphasizing the importance of considering exogenous variables alongside endogenous ones. The proposed TimeXer framework aims to bridge this gap by leveraging external information to enhance forecasting accuracy.
The model utilizes a deftly designed embedding layer that empowers the canonical Transformer architecture. This allows for the reconciliation of endogenous and exogenous data through patch-wise self-attention and variate-wise cross-attention mechanisms.
Experimental results showcase TimeXer's ability to significantly improve time series forecasting with exogenous variables across twelve real-world benchmarks. The model achieves consistent state-of-the-art performance, highlighting its effectiveness in capturing complex relationships between variables.
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by Yuxuan Wang,... at arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.19072.pdfDeeper Inquiries