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itsmr (version 1.10)

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

1.10

License

FreeBSD

Maintainer

George Weigt

Last Published

August 6th, 2022

Functions in itsmr (1.10)

Number of sunspots, 1770 to 1869

Number of international airline passengers, 1949 to 1960

Autocovariance of data

Find the best model from a range of possible ARMA models

Compute residuals

Estimate AR coefficients using the Burg method

Compute AR infinity coefficients

Forecast using ARAR algorithm

Autocovariance of ARMA model

Estimate ARMA model coefficients using maximum likelihood

Estimate MA coefficients using the innovations algorithm

USA accidental deaths, 1973 to 1978

Check for causality and invertibility

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

Run a self test

Estimate AR coefficients using the Yule-Walker method

Estimate seasonal component

Apply a moving average filter

Apply a spectral filter

Apply a low pass filter

Time Series Analysis Using the Innovations Algorithm

Generate synthetic observations

Estimate ARMA coefficients using the Hannan-Rissanen algorithm

Estimate harmonic components

Level of Lake Huron, 1875 to 1972

Forecast future values

Apply an exponential filter

Plot spectrum of data or ARMA model

Australian red wine sales, January 1980 to October 1991

Plot one or two time series

Estimate trend component

Compute MA infinity coefficients

Specify an ARMA model

Plot a periodogram

Plot data and/or model ACF and PACF

USA union strikes, 1951-1980

Test residuals for stationarity and randomness