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#Com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess Classes and Interfaces - 24 results found.
| Name | Description | Type | Package | Framework |
| AdditiveModel | The additive model of a time series is an additive composite of the trend, seasonality and irregular random components. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess | SuanShu |
| AR1GARCH11Model | An AR1-GARCH11 model takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch | SuanShu |
| ARMAFit | Interface | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu | |
| ARMAForecast | Forecasts an ARMA time series using the innovative algorithm. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
| ARMAForecastMultiStep | Computes the h-step ahead prediction of a causal ARMA model, by the innovative algorithm. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
| ARMAForecastOneStep | Computes the one-step ahead prediction of a causal ARMA model, by the innovative algorithm. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
| ARMAGARCHFit | This implementation fits, for a data set, an ARMA-GARCH model by Quasi-Maximum Likelihood "QMLE" stands for Quasi-Maximum Likelihood Estimation, which assumes Normal distribution and | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch | SuanShu |
| ARMAGARCHModel | An ARMA-GARCH model takes this form: X_t = mu + sum_{i=1}^p phi_i X_{t-i} + sum_{i=1}^q heta_j epsilon_{t-j} + epsilon_t, | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch | SuanShu |
| ARMAModel | A univariate ARMA model, Xt, takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
| ARMAXModel | The ARMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
| ARModel | This class represents an AR model. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
| AutoCorrelation | Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that This implementation solves the Yule-Walker equation. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
| AutoCovariance | Computes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by The R equivalent functions are ARMAacf and TacvfAR in package FitAR. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
| ConditionalSumOfSquares | The method Conditional Sum of Squares (CSS) fits an ARIMA model by minimizing the conditional sum of squares. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
| GARCH11Model | An GARCH11 model takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch | SuanShu |
| GARCHFit | This implementation fits, for a data set, a Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch | SuanShu |
| GARCHFit .GRADIENT | the available methods to compute the gradient to guild the optimization searchuse the analytical gradient formulae in the references, eqs. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch | SuanShu |
| GARCHModel | The GARCH(p, q) model takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch | SuanShu |
| GARCHSim | This class simulates the GARCH models of this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch | SuanShu |
| 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 | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess | SuanShu |
| LinearRepresentation | The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
| MADecomposition | This class decomposes a time series into the trend, seasonal and stationary random components using the Moving Average Estimation method with symmetric window. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess | SuanShu |
| MAModel | This class represents a univariate MA model. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
| MultiplicativeModel | The multiplicative model of a time series is a multiplicative composite of the trend, seasonality and irregular random components. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess | SuanShu |