What does Single Exponential Smoothing (SES) primarily use to forecast future values?

Single Exponential Smoothing (SES) primarily utilizes a weighted average of past time-series values to forecast future values. This method places more importance on the most recent observations while still incorporating older data, albeit with less weight. As new data becomes available, SES updates its forecast by recalibrating the weights assigned to previous values, ensuring that the forecast remains responsive to the latest trends in the data.

The key aspect of SES is its use of a smoothing constant, which determines how much weight to give to the most recent observation compared to the past data. This allows SES to effectively balance the need for responsiveness to changes in the data series while maintaining a level of stability in the forecast. The flexibility of the smoothing constant enables practitioners to fine-tune their forecasts according to the volatility and patterns within the specific time series they are analyzing.