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itsmr (version 1.9)

Time Series Analysis Using the Innovations Algorithm

Description

Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis. .

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Version

Install

install.packages('itsmr')

Monthly Downloads

572

Version

1.9

License

FreeBSD

Maintainer

George Weigt

Last Published

September 10th, 2018

Functions in itsmr (1.9)

Find the best model from a range of possible ARMA models

Estimate AR coefficients using the Burg method

Compute AR infinity coefficients

Estimate MA coefficients using the innovations algorithm

Level of Lake Huron, 1875 to 1972

Time Series Analysis Using the Innovations Algorithm

Apply a spectral filter

Specify an ARMA model

Autocovariance of ARMA model

Compute MA infinity coefficients

Autocovariance of data

Plot a periodogram

Estimate ARMA coefficients using the Hannan-Rissanen algorithm

Plot data and/or model ACF and PACF

Estimate trend component

Estimate harmonic components

Australian red wine sales, January 1980 to October 1991

Compute residuals

Plot one or two time series

Forecast using ARAR algorithm

Estimate ARMA model coefficients using maximum likelihood

Plot spectrum of data or ARMA model

Number of sunspots, 1770 to 1869

Apply a low pass filter

Dow Jones utilities index, August 28 to December 18, 1972

Apply an exponential filter

Estimate AR coefficients using the Yule-Walker method

Forecast future values

Generate synthetic observations

Check for causality and invertibility

Estimate seasonal component

USA accidental deaths, 1973 to 1978

Run a self test

Apply a moving average filter

USA union strikes, 1951-1980

Test residuals for stationarity and randomness

Number of international airline passengers, 1949 to 1960