| Class | Description |
|---|---|
| AdditiveModel |
The additive model of a time series is an additive composite of the trend, seasonality and irregular random components.
\[
Y[t] = T[t] + S[t] + e[t]
\]
|
| InnovationsAlgorithm |
The innovations algorithm is an efficient way to obtain a one step least square linear predictor
for a univariate linear time series with known auto-covariance and these properties (not limited
to ARMA processes):
\(\{x_{t}\}\) can be non-stationary.
|
| MADecomposition |
This class decomposes a time series into the trend, seasonal and stationary random components
using the Moving Average Estimation method with symmetric window.
|
| MultiplicativeModel |
The multiplicative model of a time series is a multiplicative composite of the trend, seasonality and irregular random components.
\[
Y[t] = T[t] \cdot S[t] \cdot e[t]
\]
|
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