Forecasting Routines for Locally Stationary Wavelet Processes

Documentation for package ‘forecastLSW’ version 1.0

forecastLSW-package	Forecasting for locally stationary (wavelet) time series based on the local partial autocorrelation function.
abml	Gross Value Added (GVA, Average) at basis prices: CP SA time series / second differenced series
abmld2	Gross Value Added (GVA, Average) at basis prices: CP SA time series / second differenced series
analyze.abmld2	Analyzes the abmld2 data, see below for more details.
analyze.windanomaly	Analyzes the windanomaly data, see below for more details.
dforecastlpacf	Forecasts future values of the time series 'x' 'h'-steps ahead. (for the specified horizon 'h') using the lpacf to decide the dimension of the generalized Yule-Walker equations.
forecastlpacf	Forecasts future values of the time series 'x' 'h'-steps ahead. (for the specified horizon 'h') using the lpacf to decide the dimension of the generalized Yule-Walker equations.
forecastpanel	Function to produce a plot of data forecasts.
fp.forecast	Do automatic Box-Jenkins ARIMA fit and forecast.
plot.forecastlpacf	Plot the results of forecasting using 'forecastlpacf'
print.forecastlpacf	Prints a 'forecastlpacf' object
summary.forecastlpacf	Print out summary information about a 'forecastlpacf' object
testforecast	Compare locally stationary forecasting with Box-Jenkins-type forecasting, by predicting the final values of a time series.
which.wavelet.best	Find out what wavelet is good for forecasting your series.
windanomaly	Eq. Pacific meridional wind anomaly index, Jan 1900 - June 2005