R: Time Series Analysis Using the Innovations Algorithm

# Time Series Analysis Using the Innovations Algorithm

## Documentation for package ‘itsmr’ version 1.11

- DESCRIPTION file

## Help Pages

itsmr-package | Time Series Analysis Using the Innovations Algorithm
aacvf | Autocovariance of ARMA model
acvf | Autocovariance of data
airpass | Number of international airline passengers, 1949 to 1960
ar.inf | Compute AR infinity coefficients
arar | Forecast using ARAR algorithm
arma | Estimate ARMA model coefficients using maximum likelihood
autofit | Find the best model from a range of possible ARMA models
burg | Estimate AR coefficients using the Burg method
check | Check for causality and invertibility
deaths | USA accidental deaths, 1973 to 1978
dowj | Dow Jones utilities index, August 28 to December 18, 1972
forecast | Forecast future values
hannan | Estimate ARMA coefficients using the Hannan-Rissanen algorithm
hr | Estimate harmonic components
ia | Estimate MA coefficients using the innovations algorithm
itsmr | Time Series Analysis Using the Innovations Algorithm
lake | Level of Lake Huron, 1875 to 1972
ma.inf | Compute MA infinity coefficients
periodogram | Plot a periodogram
plota | Plot data and/or model ACF and PACF
plotc | Plot one or two time series
plots | Plot spectrum of data or ARMA model
Resid | Compute residuals
season | Estimate seasonal component
selftest | Run a self test
sim | Generate synthetic observations
smooth.exp | Apply an exponential filter
smooth.fft | Apply a low pass filter
smooth.ma | Apply a moving average filter
smooth.rank | Apply a spectral filter
specify | Specify an ARMA model
strikes | USA union strikes, 1951-1980
Sunspots | Number of sunspots, 1770 to 1869
test | Test residuals for stationarity and randomness
trend | Estimate trend component
wine | Australian red wine sales, January 1980 to October 1991
yw | Estimate AR coefficients using the Yule-Walker method