Types of forecasting models:
(1) Average models:
The predictions of all future values are equal to the past data.
(2)
Naive models:
Forecasts are produced that are equal to the last observed data.
(3)
Drift models:
The forecasts are allowed to increase or decrease over time, where the amount of change over time is set be the average change seen the historical data.
(4)
Seasonal Naive Models:
Account for seasonality by setting each prediction to be equal to the last observed value of the same season.
(5)
Time Series Models:
Use historical data as the basis of estimating future outcomes:
(i) Moving average
(ii) Weighted Moving Average
(iii) Kalmen Filtering
(iv) Exponential Smoothing
(v) Auto-Regressive Moving Average (ARMA)
(vi) Seasonal ARIMA
(vii) Extrapolation
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