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Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a method to make Time Series Foundation Models (TSFMs) more transparent for critical infrastructure applications like power grids. Their approach uses Shapley Additive Explanations (SHAP) to explain model predictions by selectively withholding inputs, allowing for scalable analysis. Evaluations on day-ahead load forecasting showed TSFMs like Chronos-2 and TabPFN-TS performed competitively and their explanations aligned with domain knowledge, demonstrating their potential as reliable tools. AI

IMPACT Enhances trust in AI for critical infrastructure forecasting, enabling wider adoption in energy systems.

RANK_REASON Academic paper on explainability for time series foundation models.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Matthias Hertel, Alexandra Nikoltchovska, Sebastian P\"utz, Ralf Mikut, Benjamin Sch\"afer, Veit Hagenmeyer ·

    Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models

    arXiv:2604.28149v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in energy systems. However, applications in critical infrastructure like power grids …

  2. arXiv cs.LG TIER_1 English(EN) · Veit Hagenmeyer ·

    Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models

    Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in energy systems. However, applications in critical infrastructure like power grids require transparency to ensure trust and reliabi…

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