MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a time-series forecasting model using the built-in DeepAR algorithm. The training data consists of daily sales for 1000 products over 2 years. The model performs well on most products, but for a few products with intermittent demand (sporadic sales), the predictions are poor. Which action should the data scientist take to improve predictions for these products?
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
✓
Create a separate forecasting model specifically for intermittent demand products, using a model designed for such patterns (e.g., Croston's method).
Intermittent demand patterns (sporadic sales) require specialized models like Croston's method, which are designed to handle non-continuous demand. Option B is wrong because a simple linear regression model cannot capture the irregular spikes of intermittent demand; such models assume continuous, steady patterns. Option C is wrong because increasing the context length of DeepAR, which is built for regular time series, does not address the fundamental issue of sporadic demand—the model still expects continuous values. Option D is wrong because adding unrelated product categories introduces noise and does not help the model learn the specific intermittent pattern of the target products.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a separate forecasting model specifically for intermittent demand products, using a model designed for such patterns (e.g., Croston's method).
Why this is correct
Intermittent demand requires specialized models like Croston's method or TSB.
- ✗
Use a linear regression model for all products.
Why it's wrong here
Linear regression is unlikely to capture the complex patterns of intermittent demand.
- ✗
Increase the context length of the DeepAR model to capture longer history.
Why it's wrong here
More history may not help because the demand is sporadic, not dependent on long patterns.
- ✗
Add more training data by including additional product categories.
Why it's wrong here
More data does not address the fundamental issue of intermittent demand behavior.
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