MLS-C01 Modeling Practice Question
A data scientist is training a time series forecasting model using Amazon SageMaker's DeepAR algorithm. The dataset contains daily sales data for 10,000 products over 2 years. The scientist splits the data chronologically: training on the first 18 months, validation on the next 3 months, and test on the last 3 months. The model performs well on validation but poorly on test. The data scientist suspects the model is overfitting to the validation period. Which action should the scientist take to improve test performance?
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
✓
Use time series cross-validation with an expanding window
Time series cross-validation with an expanding window evaluates the model on multiple validation periods, ensuring robustness and reducing overfitting to a single validation window. Option B reduces context length and may lose long-term dependencies. Option C may introduce irrelevant features and does not address overfitting to validation. Option D removes validation entirely, which would not help diagnose or reduce overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use time series cross-validation with an expanding window
Why this is correct
Correct. Expanding window cross-validation uses multiple validation periods, reducing overfitting to a single validation window and improving generalization to the test period.
- ✗
Reduce the context length to 30 days
Why it's wrong here
Incorrect. Reducing context length to 30 days may lose long-term patterns and does not directly address overfitting to the validation period.
- ✗
Add more exogenous features like holidays and promotions
Why it's wrong here
Incorrect. Adding more exogenous features could introduce noise and may worsen overfitting; it does not specifically address overfitting to the validation period.
- ✗
Use the entire dataset for training and ignore validation
Why it's wrong here
Incorrect. Using the entire dataset for training and ignoring validation removes the ability to detect overfitting and may still result in poor test performance.
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