XTRTimeS — eXplainable Transformer for Robust Time Series Forecasting

Unreliable predictions limit the usability of AI in pervasive systems, especially in time series forecasting of critical sectors like medicine. Incomplete data and missing measurements degrade model performance and obscure their impact on predictions. Hardware failures and limited resources to collect data at required frequencies exacerbate these challenges. This research proposes XTRTimeS—eXplainable Transformer for Robust Time Series forecasting—an approach that leverages explainability techniques to improve forecasting with incomplete data while providing a confidence evaluation. The evaluation offers users insights into how missing data influence predictions, highlighting both likely and extreme scenarios. Additionally, it can be employed in machine-to-machine interactions, where predictions with low confidence are automatically discarded. By enhancing transparency and robustness, XTRTimeS supports reliable time series predictions, particularly in high-stakes applications such as healthcare and autonomous systems.