Train-Test Split vs. Time-Series Cross-Validation
This tests your grasp of data leakage in temporal data. A good answer explains why random splits create lookahead bias, then details how rolling-origin validation respects time. A red flag is just describing methods without explaining *why* one is necessary.
This tests your understanding of data leakage and temporal dependency. A great answer first explains how random splits violate time order, leading to lookahead bias and invalid metrics. Then, it contrasts this with a time-series approach like rolling-origin validation, where the training set always precedes the test set, simulating real-world forecasting. A major red flag is simply describing the mechanics of each method without connecting it back to the core problem of using future data to predict the past.
Read the original → otexts.com
- #time series
- #ml
- #model evaluation
- #cross-validation
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