Random split vs walk-forward validation in forecasting
WHAT IT TESTS: Awareness of temporal leakage. ANSWER OUTLINE: Random splits leak future data into training; walk-forward validation rolls the origin ahead, testing only on later observations. RED FLAG: Claiming random splits work for time-series.
WHAT IT TESTS: Whether you recognize that random train-test splits destroy temporal structure, causing information leakage and optimistically biased forecast accuracy. ANSWER OUTLINE: Random splits let models train on future values and test on past ones; walk-forward validation rolls the origin forward, using only past data to predict the next observation and averaging errors across multiple folds.
Read the original → otexts.com
- #time series
- #cross-validation
- #forecasting
- #model evaluation
- #temporal leakage
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