A time series obtained from quarterly data exhibits an increasing linear trend and increasing seasonal variation.Which one of the following would be the most appropriate way to model this time series?
A)Simple exponential smoothing.
B)Multiplicative decomposition.
C)Box-Jenkins methodology.
D)Holt-Winters' double exponential smoothing model.
E)Durbin-Watson statistic.

Question

Quizplus · Accepted Answer

Since the time series exhibits both an increasing linear trend and increasing seasonal variation, the most appropriate way to model this time series would be multiplicative decomposition. This method can account for both trend (linear in this case) and seasonality (increasing) by decomposing the time series into the product of trend, seasonal, and irregular components. Other methods, such as simple exponential smoothing and Holt-Winters' double exponential smoothing model, can handle either trend or seasonality but not both simultaneously. Box-Jenkins methodology is a broader approach that requires identifying a suitable ARIMA model for the time series, which may or may not capture both trend and seasonality. The Durbin-Watson statistic is a diagnostic tool for autocorrelation in the residuals of a time series model and is not relevant for selecting a modeling approach.

A Time Series Obtained from Quarterly Data Exhibits an Increasing