Exponential smoothing

Exponential smoothing is an approach to smoothing time series data using an exponentially weighted moving average (EWMA). It is used to forecast output levels and to identify any underlying trends, seasonality, or cyclical behavior in the data set. It can be used for both short-term and long-term forecasting.

The main idea behind exponential smoothing is to give more recent data points greater weight when computing the forecast than older data points. This is done by assigning exponentially decreasing weights to the data points as they get further back in the sequence of values. The original earliest data points in the sequence are assigned the smallest weight, while the most recent data points in the sequence are assigned the highest.

The method can be used to forecast values with a single smoothing parameter for the entire data set, or with multiple smoothing parameters that correspond to different seasonal periods or holiday periods in the data. This allows the algorithm to be very flexible when it comes to dealing with data that has interesting patterns or cycles.

Exponential smoothing is used in many applications, including inventory planning, forecasting sales and customer demand, and designing economic models. With its emphasis on recent data, it is especially effective for observing trends with data that experiences rapid changes in a short time. It has the advantage of being a relatively simple technique with clear parameters and reasonable computations, while also being very effective.