Time Series and Forecasting
Computes least-squares or method-of-moments estimates of parameters and optionally computes forecasts and their associated probability limits.
Sample autocorrelation function.
Automatic selection and fitting of a univariate autoregressive time series model.
Automatically identifies time series outliers, determines parameters of a multiplicative seasonal ARIMA model, and produces forecasts that incorporate the effects of outliers whose effects persist beyond the end of the series.
Perform a Box-Cox transformation.
Computes the sample cross-correlation function of two stationary time series.
Performs differencing on a time series.
Estimates missing values in a time series.
Compute estimates of the parameters of a GARCH(p,q) model.
Performs Kalman filtering and evaluates the likelihood function for the state-space model.
Lack-of-fit test based on the correlation function.
Exact maximum likelihood estimation of the parameters in a univariate ARMA (autoregressive, moving average) time series model.
Computes the multichannel cross-correlation function of two mutually stationary multichannel time series.
Sample partial autocorrelation function.
Estimates the optimum seasonality parameters for a time series using an autoregressive model, AR(p), to represent the time series.
Computes forecasts, their associated probability limits and y weights for an outlier contaminated time series whose underlying outlier free series follows a general seasonal or nonseasonal ARMA model.
Detects and determines outliers and simultaneously estimates the model parameters in a time series whose underlying outlier free series follows a general seasonal or nonseasonal ARMA model.
