JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements
Quick summary
arXiv:2609.36966v1 Announce Type: cross Abstract: 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 covariat
Key takeaways
- arXiv:2609.36966v1 Announce Type: cross Abstract: 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.
Why it matters
“JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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