Time series forecasting is a critical application of AI, and the paper titled TimeXer presents a new approach that incorporates both endogenous and exogenous variables for improved accuracy. It amends the Transformer architecture to process external variables, which is pivotal for real-world forecasting tasks where a system’s variables are interdependent.

Contributions:

• Introduces TimeXer, a novel framework for handling time series forecasting with exogenous variables.
• Embeds a specifically designed layer in the Transformer to handle the integration of different types of series data.
• Employs patch-wise self-attention and variate-wise cross-attention mechanisms for better information reconciliation.

Enhanced Forecasting Performance:

• Accuracy: Significantly better forecasts compared to solely focusing on target (endogenous) variables.
• State-of-the-Art: Outperforms existing models on twelve real-world forecasting benchmarks, showcasing its remarkable efficacy.

TimeXer is an innovative contribution that promises to expand the utility of Transformers in time series forecasting by exploiting the often-overlooked exogenous information. This could be game-changing for industries relying on accurate forecasting, such as finance, supply chain management, and meteorology. The way TimeXer brings together disparate data streams opens up possibilities for intricate predictive models that mirror the complexities of the real world.

The full study is available here.