Package dev.nm.stat.timeseries.linear.univariate.stationaryprocess
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Class Summary Class Description 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.