A special type of exponential smoothing that takes the success of previous forecasts into account when setting a value of ALPHA for the next period. In this manner, periods that experienced high error will cause ALPHA to be set high and, thus, adjust quickly. When error is low, AS assumes the technique is doing well and sets ALPHA at a low level. This makes ES much more responsive to changes in the level of the data and less reactive to noise. The advantage of adaptive smoothing is that the decision of what value of ALPHA to use in exponential smoothing is eliminated. A disadvantage of adaptive smoothing is that trend and seasonality are ignored.
What is Adaptive Smoothing?
Adaptive smoothing is a special type of exponential smoothing technique used in forecasting. It takes into account the success of previous forecasts when determining the value of ALPHA for the next period. By doing so, adaptive smoothing adjusts the smoothing parameter, ALPHA, based on the error experienced in previous periods.
The purpose of adaptive smoothing is to make the forecasting technique more responsive to changes in the level of the data and less reactive to noise. When periods have high forecasting errors, adaptive smoothing sets ALPHA at a high level, allowing for quick adjustments. Conversely, when the error is low, adaptive smoothing assumes that the technique is performing well and sets ALPHA at a low level.
One of the advantages of adaptive smoothing is that it eliminates the need to manually decide on the value of ALPHA for exponential smoothing. This removes the subjectivity and potential errors associated with selecting an appropriate smoothing parameter. Adaptive smoothing automatically adjusts ALPHA based on the performance of the forecasting model.
However, it is important to note that adaptive smoothing ignores trend and seasonality in the data. Trend refers to the long-term direction of the data, while seasonality refers to recurring patterns that occur within a specific time frame. By not considering these factors, adaptive smoothing may not accurately capture the underlying patterns in the data.
In summary, adaptive smoothing is a technique that adjusts the smoothing parameter based on the success of previous forecasts. It makes exponential smoothing more responsive to changes in the data and less reactive to noise. While it eliminates the need for manual selection of ALPHA, it does not account for trend and seasonality in the data. As with any forecasting technique, it is important to consider the specific characteristics of the data and the goals of the analysis when deciding whether to use adaptive smoothing.