Class | Description |
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AdditiveModel |
The additive model of a time series is an additive composite of the trend, seasonality and irregular random components.
|
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):
{xt} can be non-stationary.
E(xt) = 0 for all t.
This class implements the part of the innovations algorithm that computes the prediction error
variances, v and prediction coefficients θ.
|
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.
|
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