Zero-Shot Generalization with CovLift: Context and Covariate Capacity

Time-series foundation models (TSFMs) promise accurate zero-shot forecasts on unseen series. Frozen models can nevertheless fail to use predictive information already available in their inputs. We investigate how context length shapes the value of covariates and develop CovLift, a method for adapting inputs to frozen TSFMs. CovLift screens engineered covariates against observed history and matches features and context length to the forecasting model. Synthetic probes predict Chronos-2's covariate-to-numeric-only error ratio on unseen relationships. On spring electricity load with day-1 weather forecasts, eight added features harm Chronos-2 at 128 observations but reduce its geometric paired mean squared error ratio by 24% against same-length raw covariates at 1,024. This context effect replicates across sixteen unseen regions, persists under an assumed six-hour publication delay, and has the same direction on TiRex-2. CovLift also improves over raw covariates on unseen load regions. Summer evidence is inconclusive; tested additions show no confirmed Chronos-2 gain on solar or wind, and the original CovLift-Auto configuration slightly worsens aggregate benchmark performance. These findings identify context length as a determinant of covariate value and demonstrate how input design can improve zero-shot generalization on unseen load regions without updating model weights.