The greatest strength of the Time Series Forecasting system is the wide range of forecasting models it provides. Using the system, you can construct an appropriate forecasting model for almost any time series.
Forecasting Models
exponential smoothing
simple exponential
double exponential
linear exponential
damped-trend linear exponential
seasonal exponential
Winters smoothing, additive and multiplicative
Box-Jenkins ARIMA models, including seasonal ARIMA models
predictor variables
simple regressors
seasonal dummy variable regressors
intervention (dummy) variables to model exceptional events, level shifts, or trend shifts
adjustment variables to adjust the forecasts by fixed amounts at each period
transfer functions or dynamic regression: use transformations, lags, or time series filters to model the impact of predictor variables
automatic and user specified forecasting models for predictor variables
time trend models
linear
quadratic
cubic
logistic
logarithmic
exponential
hyperbolic
power function
exp(A+B/time)
data transformations
logarithmic
logistic
square root
Box-Cox
combining or average the predictions of other forecasting models
external (judgmental) forecasts
customized models