TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables introduces TimeXer, a robust approach to time series forecasting incorporating external sources of information.

• Introduces an innovative embedding layer to factor in exogenous variables.
• Adopts patch-wise self-attention and variate-wise cross-attention within Transformer architecture.
• Uses a global endogenous variate token to tie exogenous series with endogenous temporal patches.
• TimeXer excels in accuracy on various real-world forecasting benchmarks, setting new performance standards.

This advancement in time series forecasting suggests new ways in which evolving AI technologies can tackle the complexities of integrating diverse data sources for more accurate predictions.