MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a time series dataset of daily website traffic. The scientist notices a strong weekly seasonality. To better understand the underlying patterns, which decomposition method should the scientist use to separate the trend, seasonal, and residual components?
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 STL (Seasonal and Trend decomposition using Loess).
STL (Seasonal and Trend decomposition using Loess) is a robust method for decomposing time series into trend, seasonal, and residual components. It can handle any seasonality period, including weekly seasonality in daily data, and is robust to outliers. Option A is wrong because additive decomposition using moving averages assumes fixed seasonal amplitude and is sensitive to outliers. Option C is wrong because ARIMA is a forecasting model, not a decomposition method. Option D is wrong because ETS is an exponential smoothing framework for forecasting, not primarily for decomposition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Additive decomposition using moving averages.
Why it's wrong here
Classical decomposition is less robust to outliers.
- ✓
Use STL (Seasonal and Trend decomposition using Loess).
Why this is correct
STL is robust and flexible for any seasonality.
- ✗
Fit an ARIMA model and examine residuals.
Why it's wrong here
ARIMA is for forecasting, not decomposition.
- ✗
Apply an ETS (Error, Trend, Seasonal) model.
Why it's wrong here
ETS is a forecasting model, not a decomposition method.
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