What is the most traditional method to conduct a time series analysis?

• A. ARIMA modeling
• B. Decomposition method
• C. Fourier transform
• D. Cluster analysis

Answer: (B)

The traditional approach to time series analysis is to decompose the series into underlying components: a trend that captures long-term movement, a seasonal pattern that repeats over time, and a residual or irregular component that remains after removing trend and seasonality. This decomposition provides an intuitive way to understand and forecast data by isolating different sources of variation. It relies on methods like moving averages and seasonal adjustment, forming the foundation of early time series practice (think of classic additive or multiplicative decompositions and Holt-Winters smoothing).

In contrast, modeling with ARIMA is a more modern, model-based approach that directly specifies how past values and past errors influence current values, along with differencing to achieve stationarity. Fourier transforms are powerful for identifying frequency components but don’t alone provide a complete, interpretable time series model. Cluster analysis is not inherently a time series technique.