Which forecasting model involves double exponential smoothing?

The forecasting model that involves double exponential smoothing is indeed the use of Exponential Smoothing techniques. Double exponential smoothing, specifically, is an extension of simple exponential smoothing that accounts for trends in the data. It employs two smoothing constants – one for the level and another for the trend – making it particularly effective for time series data with a linear trend.

By applying double exponential smoothing, forecasters are able to make predictions that not only reflect the current level of the data but also incorporate the trend over time, providing a more reliable estimate in scenarios where trends are present. This model is particularly useful for datasets that exhibit consistent increases or decreases over time, allowing for sophisticated adjustments that improve forecasting accuracy.

The other forecasting methods mentioned have distinct characteristics that do not align with the double exponential smoothing technique. For example, Croston’s Method is designed for intermittent demand patterns, ARIMA combines autoregression and moving averages for stationary time series while considering seasonality and trends, and the Holt-Winters method includes both trend and seasonality in its approach but is not characterized simply as double exponential smoothing. Thus, Exponential Smoothing directly fits the requirement of the question.