JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements
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 relevan...
| Authors | Donguk Kwon, Wooseok Jeong, Dongha Lee |
|---|---|
| Primary category | cs.LG |
| Published | 29 September 2026 |
| Last updated at source | 30 September 2026 |
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