mlsanityData to 14 Oct 2026

JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements

arXiv 2609.36966 ↗First posted 29 Sep 2026cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Covariate effects vary across contexts and shift over time, requiring forecasters to assess how to use them for each forecasting context. As forecasting proceeds, observations for earlier forecasts become available, providing feedback on past covariate use for subsequent forecasts. However, when multiple covariates act together, the forecast error reveals the numerical discrepancy from the observation but not how the covariates should have been used. We introduce JudgeCast, an experience-based framework for time series forecasting with covariates. Following the judgmental adjustment practice, a frozen TSFM provides the base forecast, while a frozen LLM uses the current context and relevant experience to adjust it. Within the adjustment, assessing covariate effects and determining the numerical adjustment serve distinct roles, so JudgeCast first forms explicit covariate-wise judgments and then determines the adjustment. After observation, JudgeCast uses the observed residual of the base forecast to reconstruct alternative judgments and evaluates the original and alternatives through their resulting adjustments. The best-performing decision is selected and retained as validated experience for subsequent forecasts. Across diverse real-world datasets, JudgeCast outperforms strong baselines. Ablations show that explicit covariate-wise judgment can improve forecast-time adjustment, while residual-guided experience construction yields more reliable forecasting gains than retaining raw decisions as experience.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 45 references that are in the map. First posted on arXiv 29 Sep 2026.

Builds on

32 of 45 references.

References · 32

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  3. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  4. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
  5. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
  6. 2024Chronos: Learning the Language of Time Seriescited by 759
  7. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  8. 2023A decoder-only foundation model for time-series forecastingcited by 692
  9. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 614
  10. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 500
  11. 2024TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variablescited by 248
  12. 2020Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmarkcited by 108
20 more
  1. 2024From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflectioncited by 98
  2. 2025fev-bench: A Realistic Benchmark for Time Series Forecastingcited by 64
  3. 2024LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Promptingcited by 59
  4. 2025Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMscited by 34
  5. 2026Toto 2.0: Time Series Forecasting Enters the Scaling Eracited by 30
  6. 2024ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysiscited by 27
  7. 2024TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generationcited by 21
  8. 2025Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecastingcited by 20
  9. 2025Improving Time Series Forecasting via Instance-aware Post-hoc Revisioncited by 15
  10. 2026MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoningcited by 9
  11. 2026Unlocking the Value of Text: Event-Driven Reasoning and Multi-Level Alignment for Time Series Forecastingcited by 5
  12. 2026Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariatescited by 4
  13. 2026Explainable Load Forecasting with Covariate-Informed Time Series Foundation Modelscited by 4
  14. 2025LoFT-LLM: Low-Frequency Time-Series Forecasting with Large Language Modelscited by 3
  15. 2026Bridging the Last Mile of Time Series Forecasting with LLM Agentscited by 3
  16. 2026Rethinking Post-Training Recipes for Multimodal Time-Series Forecastingcited by 2
  17. 2026Retrieval-Corrected Conformal Prediction for Time Seriescited by 2
  18. 2024Hierarchical Neural Additive Models for Interpretable Demand Forecastscited by 1
  19. 2026RareCP: Regime-Aware Retrieval for Efficient Conformal Predictioncited by 1
  20. 2026CTRL: Control-Based Time Series Forecasting with LLM-Guided Residual Learningcited by 1

Built on by

Not cited yet.