ltsa v1.4.6

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Linear Time Series Analysis

Methods of developing linear time series modelling. Methods are given for loglikelihood computation, forecasting and simulation.

Functions in ltsa

 Name Description DLAcfToAR Autocorrelations to AR parameters DLResiduals Prediction residuals TrenchInverse compute the matrix inverse of a positive-definite Toepliz matrix TrenchMean Exact MLE for mean given the autocorrelation function PredictionVariance Prediction variance innovationVariance Nonparametric estimate of the innovation variance DLLoglikelihood Durbin-Levinsion Loglikelihood TrenchLoglikelihood Loglikelihood function of stationary time series using Trench algorithm SimGLP Simulate GLP given innovations ToeplitzInverseUpdate Inverse of Toeplitz matrix of order n+1 given inverse of order n TrenchForecast Minimum Mean Square Forecast is.toeplitz test if argument is a symmetric Toeplitz matrix tacvfARMA theoretical autocovariance function (acvf) of ARMA DHSimulate Simulate General Linear Process exactLoglikelihood Exact log-likelihood and MLE for variance ltsa-package Linear Time Series Analysis DLSimulate Simulate linear time series No Results!