The paper ‘TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables’ presents a new framework called TimeXer that seeks to leverage external information for improved time series forecasting. TimeXer employs patch-wise self-attention and variate-wise cross-attention within the Transformer architecture to reconcile endogenous and exogenous information.

• Provides a novel embedding layer designed for handling exogenous variables.
• Achieves state-of-the-art performance across multifarious real-world forecasting benchmarks.
• Utilizes a global endogenous variate token to integrate exogenous series with endogenous temporal patches.

This framework addresses a critical need for incorporating external information in time series forecasting, which could significantly enhance predictive accuracy in various practical settings.