Exponential smoothing is a
rule of thumb technique for smoothing time series data, particularly for recursively applying as many as three
low-pass filters with exponential window functions. Such techniques have broad application that is not intended to be strictly accurate or reliable for every situation. It is an easily learned and easily applied procedure for approximately calculating or recalling some value, or for making some determination based on prior assumptions by the user, such as seasonality. Like any application of repeated low-pass filtering, the observed phenomenon may be an essentially
random process, or it may be an orderly, but
noisy, process. Whereas in the
simple moving average the past observations are weighted equally, exponential
window functions assign exponentially decreasing weights over time. The use of three filters is based on empirical evidence and broad application.