| Class | Description |
|---|---|
| AdditiveModel |
The additive model of a time series is an additive composite of the trend, seasonality and irregular random components.
|
| InnovationAlgorithm |
The innovation algorithm is an efficient way of
obtaining a one step least square linear predictor for a linear time series {Xt} with known auto-covariance.
|
| MADecomposition |
This class decomposes a time series into the trend, seasonal and the stationary random components
using the Moving Average Estimation with symmetric window.
|
| MultiplicativeModel |
The multiplicative model of a time series is a multiplicative composite of the trend, seasonality and irregular random components.
|