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MLA-C01 Practice Question: A data scientist is building a time-series…
A data scientist is building a time-series forecasting model for daily sales data. The data spans two years. To evaluate the model's performance, the data scientist needs to simulate a realistic rolling forecast scenario. Which data splitting strategy should be used?
⚠ Common exam trap
MLA-C01 often tests the data leakage risk of random splits on time-series data, baiting candidates toward standard k-fold or hold-out methods that ignore temporal ordering.
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
Walk-forward validation (also called rolling-origin or expanding-window validation) repeatedly trains on past data and tests on the next time period, then moves the window forward. This mimics a realistic rolling forecast where the model is retrained as new data arrives, making it the correct choice for time-series evaluation.
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
Why this is correct
Walk-forward validation repeatedly trains on all data up to a point and tests on the immediately following window, then advances that split forward through time. This preserves chronological order and mimics how a deployed forecaster is retrained and rolled forward, unlike random or single-holdout splits.
- ✗
Random 80/20 train-test split
Why it's wrong here
Random splitting shuffles daily observations, so training rows sit chronologically after test rows, leaking future information into the model and inflating measured accuracy. It is tempting because it is the default for independent records, and would be correct for non-sequential data such as customer churn.
- ✗
Stratified k-fold cross-validation
Why it's wrong here
Stratified k-fold shuffles observations across folds, leaking future values into training and destroying temporal order, so it cannot simulate rolling forecasts. It tempts because stratification balances class proportions in cross-validation, which suits classification, not time-series forecasting.
- ✗
Hold-out split based on time (e.g., train on first 18 months, test on last 6 months)
Why it's wrong here
A single chronological hold-out cannot simulate repeated rolling-origin forecasts; it yields one fixed train/test boundary and no re-training across folds. It tempts because time-ordered hold-out does preserve temporal order, which suits a one-off final evaluation rather than rolling backtesting.
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