R: Forecasting Routines for Locally Stationary Wavelet Processes

# Forecasting Routines for Locally Stationary Wavelet Processes

## Documentation for package ‘forecastLSW’ version 1.1.1

- DESCRIPTION file
- Package NEWS

## Help Pages

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