On the use of cross-validation for time series predictor evaluation¶
Bergmeir, & Benítez. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Imputation Leakage
- Clinical prediction models — imputing missing lab or vital values using cohort-wide means where the cohort includes the held-out validation slice, inflating reported AUROC. Hiring and credit-risk analytics — imputing missing tenure, salary, or income fields with full-sample means that include the back-test or evaluation rows, biasing measured accuracy and fairness. Fraud detection — imputing missing transaction features with aggregate statistics computed across the labelled validation period. Time-series forecasting — forward-fill or smoothing across the train/test boundary, a particularly common look-ahead form of the leak.
This sourceEstablishes that letting post-boundary observations inform the fit when evaluating a time-series predictor contaminates the estimate, which is the look-ahead form of the leak.
- Clinical prediction models — imputing missing lab or vital values using cohort-wide means where the cohort includes the held-out validation slice, inflating reported AUROC. Hiring and credit-risk analytics — imputing missing tenure, salary, or income fields with full-sample means that include the back-test or evaluation rows, biasing measured accuracy and fairness. Fraud detection — imputing missing transaction features with aggregate statistics computed across the labelled validation period. Time-series forecasting — forward-fill or smoothing across the train/test boundary, a particularly common look-ahead form of the leak.
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Registry ID ref:09182b382119 · see in the full table