Exponential Smoothing Introduction
Exponential smoothing is a widely-recognized method of forecasting data and incorporates the use of a smoothing constant, also known as an alpha coefficient, or conversely, a smoothing factor. It is also used as a forecasting tool in data analysis, process control and certain engineering applications. Exponential smoothing is a technique used to generate the forecast of a time series. In essence, it uses weighted averages of past data points to build a forecast. The choice of weighting determines the algorithm used. The most famous of these algorithms include single, double, and triple exponential smoothing.
Single Exponential Smoothing
Single exponential smoothing is a weighted average calculation from the past. The weight of each data point is determined by a smoothing factor (alpha), which is used to determine the importance of the most recent data points for forecasting. For example, if we look at a three-month sales series of (1, 3, 6), we can predict the sales for the fourth month by using the last three points, weighted by the smoothing factor. The weight of the most recent data point (the current month) will be the smoothing factor multiplied by the weight of the month before.
Double Exponential Smoothing
This is a slightly more complex method that incorporates two smoothing factors, or alphas. The first alpha determines the weight of the current months data point and the second alpha is used to determine the weight of data points from previous months. In double exponential smoothing, the forecast of the current month is a weighted average offor the past three months, but with one factor determining the weights for current and past data points and the other for future months data points. The forecast for the fourth month is, in essence, an average of the first three months data points and the forecast of the fourth month.
Triple Exponential Smoothing
Triple exponential smoothing uses three smoothing factors, or alphas, to generate the forecast of a time series. The first alpha determines the weights of the current months data point while the second and third alphas are used to determine the weights of data points from past and future months. As in double exponential smoothing, the forecast of the current month is a weighted average of the past three months data points and the forecast of the fourth month.
Advantages of Exponential Smoothing
Exponential smoothing has several advantages. The main advantage of exponential smoothing is the ability to generate accurate forecasts of future data points. The use of multiple smoothing parameters makes it possible to accurately project future trends even when the data displays large fluctuations. Additionally, exponential smoothing requires much less data than other methods, such as autoregressive integrated moving average (ARIMA) models, which require substantial training data for accurate predictions.
Disadvantages of Exponential Smoothing
The main disadvantage of exponential smoothing is the limited scope of the forecasts it can generate. Because of the use of past data in the forecasting process, exponential smoothing forecasts are based on short-term observations and are not able to accurately project long-term trends or account for sudden changes in the data. Additionally, exponential smoothing is not suitable for forecasting data that contains seasonality or trends.
Conclusion
Exponential smoothing is an effective forecasting tool for short-term forecasting and forecasting of volatile data. It is easy to use, requiring only a simple implementation of a smoothing parameter or alpha coefficient. Despite its simplicity, however, it is an accurate short-term forecasting tool that can be used in a variety of applications. It is also a fairly simple technique to understand and is suitable for people who do not have a strong background in data analysis and forecasting.
