Exponential smoothing is a popular forecasting method within time series analysis. Compared to simple moving averages, exponential smoothing gives higher weight to recent data and a lower weight to old data. This makes it more suitable to capture recent trends and has better accuracy than simpler methods.
Since the exponential smoothing algorithm is simple in nature, it’s applied to various types of time series data for forecasting purposes. Not only is it suitable for time series data with linear behaviors, but also for those with trends and seasonality.
- Simple exponential smoothing (SES): for datasets without trends or seasonality, simple exponential smoothing algorithms can be used
- Holt’s linear exponential smoothing (HTS): for datasets with a strong trend, Holt’s linear exponential smoothing algorithm is used where the trend component is added to the simple exponential smoothing model. For those with exponential trends, a modified version known as the double exponential smoothing algorithm is often used
- Holt-Winters exponential smoothing (WMS): to account for seasonality, Holt-Winters exponential smoothing algorithm is used. The forecasting component consists of the initial observation plus the trend component and the seasonal component
While exponential smoothing algorithms are intuitive and easy to tune given the small number of parameters, it can primarily only handle non-stationary and relatively small datasets. It is more suitable for situations where computational power is limited and speed is critical. For more complex datasets, other models like ARIMA may be more applicable.
Exponential smoothing is widely used across many use cases involving time series data.
- Demand forecasting: exponential smoothing algorithms are often used to forecast customer demand and predict inventory and storage needs. Since sales data has clear seasonality, Holt-Winters exponential smoothing algorithm is used to capture that component
- Finance: even though exponential smoothing algorithms are not as accurate as other more sophisticated algorithms, given the speed of which it can generate a forecasting model, it can be used as a baseline to guide analysts in their analysis
- Internet-of-Things (IoT): weather data usually works well with exponential smoothing algorithms given their seasonality. Meteorologists or hydrologists can use exponential smoothing algorithms to forecast temperatures and precipitation levels