Editing Forecasting (section)

===Time series methods===
[[Time series]] methods use historical data as the basis of estimating future outcomes. They are based on the assumption that past demand history is a good indicator of future demand.

*[[Moving average]]
*[[Weighted moving average]]
*[[Exponential smoothing]]
*[[Autoregressive–moving-average model|Autoregressive moving average (ARMA)]] (forecasts depend on past values of the variable being forecast and on past prediction errors)
*[[Autoregressive integrated moving average|Autoregressive integrated moving average (ARIMA)]] (ARMA on the period-to-period change in the forecast variable)
:e.g. [[Box–Jenkins]] 
:Seasonal ARIMA or SARIMA or ARIMARCH,
*[[Extrapolation]]
*[[Linear prediction]]
*[[Trend estimation]] (predicting the variable as a linear or polynomial function of time)
*[[Growth curve (statistics)]]
*[[Recurrent neural network]]