DA0-002 Data Analysis Practice Question
A retail company wants to forecast monthly sales for the next 12 months. Sales data shows a clear upward trend and seasonal patterns that repeat yearly. Which time series model is most appropriate?
⚠ Common exam trap
Many candidates choose ARIMA or SARIMA because they are more 'advanced,' but the question specifically describes clear trend and seasonality without requiring stationarity or differencing, making Holt-Winters the most direct and appropriate choice.
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
✓
Holt-Winters exponential smoothing
The Holt-Winters exponential smoothing model (option C) is the most appropriate because it explicitly captures both trend and seasonality components, which are present in the sales data (upward trend and yearly seasonal patterns). Unlike simple exponential smoothing, Holt-Winters includes additive or multiplicative seasonal terms, making it ideal for data with clear, repeating seasonal cycles over a 12-month horizon.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SARIMA
Why it's wrong here
SARIMA models time series by differencing to achieve stationarity, effectively removing trends and seasonality before applying AR and MA components. This makes it tempting for data exhibiting both characteristics. However, for a clear upward trend, SARIMA does not explicitly model or forecast the trend component as a separate, evolving factor. Instead, it removes the trend through differencing, which can sometimes oversimplify the underlying trend dynamics compared to models that explicitly track and project the trend and seasonal components.
- ✗
Simple exponential smoothing
Why it's wrong here
Simple exponential smoothing does not handle trend or seasonality.
- ✓
Holt-Winters exponential smoothing
Why this is correct
Holt-Winters includes trend and seasonality components, making it suitable for this data.
- ✗
ARIMA
Why it's wrong here
ARIMA handles trend but not seasonality.
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