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MLA-C01 Practice Question: An ML team is preparing time-series data for a…
An ML team is preparing time-series data for a demand forecasting model. They want to evaluate model performance over time without leaking future information into past training windows. Which data splitting strategy is MOST appropriate?
⚠ Common exam trap
Watch out — candidates often default to random k-fold cross-validation (Option A) because it is a standard technique for i.i.d. data, forgetting that time-series data requires strict temporal ordering to avoid data leakage.
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 with an expanding window
Walk-forward validation with an expanding window is the most appropriate strategy because it respects the temporal order of the data, ensuring that each training window contains only past observations and each validation window contains only future observations. This prevents data leakage and provides a realistic evaluation of how the model will perform on unseen future time steps, which is critical for demand forecasting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Random k-fold cross-validation
Why it's wrong here
Random splits would mix future data into training, causing leakage.
- ✗
Single hold-out set with random selection
Why it's wrong here
Random hold-out may introduce leakage and does not evaluate temporal stability.
- ✗
Stratified sampling based on the target variable
Why it's wrong here
Stratified sampling does not preserve time order.
- ✓
Walk-forward validation with an expanding window
Why this is correct
Walk-forward validation trains on past data and tests on immediate future data, respecting temporal dependencies.
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