Exponential Smoothing

Exponential smoothing is a more advanced form of time series forecasting. Unlike moving averages, exponential smoothing methods can capture trends and recurring patterns. They accomplish this by:

Emphasizing the more recent data (as opposed to a moving average which weights all data equally), and
Smoothing out fluctuations, which are often caused by pure randomness in the data (or “noise” in the system)
Forecasters determine the forecast weights, controlling how fast or slow the model responds to demand changes in your actuals. Not all exponential smoothing methods can handle seasonality or other recurring patterns.

Exponential smoothing forecasting methods include:

  • Simple exponential smoothing
  • Holt’s linear method
  • Winters’ multiplicative season
  • Winters’ additive season
  • Croston’s intermittent demand
  • ARIMA (Box-Jenkins)

Other forecasting methods include: