Box-Jenkins methodology transforms nonstationary time series values into stationary time series values.

Question

Quizplus · Accepted Answer

The Box-Jenkins methodology involves a series of steps, one of which is data transformation to achieve stationarity. This is necessary because many time series models rely on the assumption of stationarity, which means that the statistical properties of the series remain constant over time. Nonstationary series, by contrast, show trends, seasonality, or other patterns that change over time, making it difficult to identify underlying patterns or forecast future values. The Box-Jenkins approach includes various techniques such as differencing, logarithmic or power transformations, seasonal adjustments, and others to achieve stationarity.

Box-Jenkins Methodology Transforms Nonstationary Time Series Values into Stationary Time