MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with a time series component. They suspect there is a weekly seasonality. Which technique should they use to confirm this?
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
✓
Compute autocorrelation function (ACF)
The autocorrelation function (ACF) measures the correlation between a time series and its lagged values. A significant spike at lag 7 confirms weekly seasonality. Option A (line chart) is subjective and not a definitive test. Option C (Fourier transform) identifies frequency components but is more complex and less direct for confirming seasonality. Option D (moving average) smooths the series and may obscure seasonality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Plot the time series line chart
Why it's wrong here
A line chart can visually suggest seasonality but does not provide statistical confirmation.
- ✓
Compute autocorrelation function (ACF)
Why this is correct
Correct. ACF at lag 7 shows the correlation with the value 7 days earlier; a significant positive autocorrelation indicates weekly seasonality.
- ✗
Perform Fourier transform
Why it's wrong here
Fourier transform can identify periodic components but is not the most straightforward method to confirm a specific periodicity like weekly.
- ✗
Compute a 7-day moving average
Why it's wrong here
A moving average smooths the series, which can reduce seasonal effects, making it harder to detect.
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