This tool in Risk Simulator deseasonalizes and detrends your original data to take out any seasonal and trending components. In forecasting models, the process of removing the effects of accumulating datasets from seasonality and trend to show only the absolute changes in values and to allow potential cyclical patterns to be identified after removing the general drift, tendency, twists, bends, and effects of seasonal cycles of a set of time-series data. For example, a detrended dataset may be necessary to see a more accurate account of a company’s sales in a given year more clearly by shifting the entire dataset from a slope to a flat surface to better see the underlying cycles and fluctuations.
Many time-series data exhibit seasonality where certain events repeat themselves after some time period or seasonality period (e.g., ski resorts’ revenues are higher in winter than in summer, and this predictable cycle will repeat itself every winter). Seasonality periods represent how many periods would have to pass before the cycle repeats itself (e.g., 24 hours in a day, 12 months in a year, 4 quarters in a year, 60 minutes in an hour, and so forth). This tool deseasonalizes and detrends your original data to take out any seasonal components. A seasonal index greater than 1 indicates a high period or peak within the seasonal cycle and a value below 1 indicates a dip in the cycle.