Time Series Analysis Using the Innovations Algorithm
USA accidental deaths, 1973 to 1978
Dow Jones utilities index, August 28 to December 18, 1972
Time Series Analysis Using the Innovations Algorithm
Level of Lake Huron, 1875 to 1972
Compute MA infinity coefficients
Plot a periodogram
Plot data and/or model ACF and PACF
Plot one or two time series
Plot spectrum of data or ARMA model
Compute residuals
Estimate seasonal component
Run a self test
Generate synthetic observations
Apply an exponential filter
Apply a low pass filter
Apply a moving average filter
Apply a spectral filter
Specify an ARMA model
USA union strikes, 1951-1980
Number of sunspots, 1770 to 1869
Test residuals for stationarity and randomness
Estimate trend component
Australian red wine sales, January 1980 to October 1991
Estimate AR coefficients using the Yule-Walker method
Forecast future values
Estimate ARMA coefficients using the Hannan-Rissanen algorithm
Estimate harmonic components
Estimate MA coefficients using the innovations algorithm
Autocovariance of ARMA model
Autocovariance of data
Number of international airline passengers, 1949 to 1960
Compute AR infinity coefficients
Forecast using ARAR algorithm
Estimate ARMA model coefficients using maximum likelihood
Find the best model from a range of possible ARMA models
Estimate AR coefficients using the Burg method
Check for causality and invertibility
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. <https://link.springer.com/book/10.1007/b97391>.