hardMultiple Choice
MLA-C01 Practice Question: A team is building a time-series forecasting…
A team is building a time-series forecasting model for daily sales data. They want to evaluate model performance using cross-validation while respecting the temporal order of the data. Which data splitting strategy should they use?
⚠ Common exam trap
MLA-C01 often tests the misconception that standard k-fold or random holdout is acceptable for time-series — candidates overlook temporal leakage and pick random splitting strategies that violate the time order.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Walk-forward validation (time-series split)
Walk-forward validation (time-series split) respects the temporal order by training on past data and validating on future data, expanding the training window forward in time. This mimics real forecasting conditions and prevents data leakage from future observations into the training set. It is the correct strategy for time-series cross-validation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Walk-forward validation (time-series split)
Why this is correct
Walk-forward validation, implemented as time-series split, trains on earlier observations and validates on later ones, preserving chronological order. Standard k-fold shuffles data and leaks future information into training, which would produce misleadingly optimistic forecasts for daily sales.
- ✗
Holdout with a random 80/20 split
Why it's wrong here
A random 80/20 holdout can place later dates in training and earlier dates in validation, so the model learns from the future and evaluation is contaminated. It is tempting because holdout splitting is simple and common, but it is only valid when observations are independent, not when temporal ordering must be respected.
- ✗
Random k-fold cross-validation
Why it's wrong here
Random k-fold shuffles observations across folds, so training folds contain future dates relative to validation folds, leaking temporal information and inflating accuracy. It is tempting because k-fold gives every point a turn at validation, but that benefit requires exchangeable data; time-series needs forward-chaining splits that preserve chronological order.
- ✗
Stratified sampling
Why it's wrong here
Stratified sampling preserves class proportions across folds but still shuffles records, mixing past and future observations in training and validation sets. It is tempting because stratification improves class balance for imbalanced classification, yet it does not enforce chronological ordering, which is the requirement here.
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