analyze.windanomaly: Analyzes the windanomaly data, see below for more details.

In forecastLSW: Forecasting Routines for Locally Stationary Wavelet Processes

Analyzes the windanomaly data, see below for more details.

Description

Takes the windanomaly data and analyzes it.

Usage

analyze.windanomaly(h=10,atTime=NULL,atLag=NULL)

Arguments

h	Numeric vector for a h-steps ahead forecast. In reality we treat the data[1:(length(data)-h)] as known and try to forecast h-steps ahead from data[length(data)-h]
atTime	Vector of the times (rows) of the lpacf to be plotted. Note that not all times can be plotted, the range of plausible values depends on the bandwidth selected for the data. At the time of writing binwidth for windanomaly is 1173 and thus the plausible values are [587,680].
atLag	Vector of the lags (columns) of the lpacf to be plotted. The default maximum lag is floor(10 * log10(n)) which is 31 for windanomaly.

Details

Takes the windanomaly data and analyzes it. Specifically the following is produced:

• time series plot of the windanomaly data

• the lpacf for the windanomaly data

• plots of the lpacf + CI for the specified times and lags

• the forecast for h to last data point(s) using the lpacf method

• the forecast for h to last data point(s) using the standard ARMA method

• plot of the original data, forecasts and confidence intervals for both methods, red=lpacf, blue=ARMA.

Value

List containing the lpacf, forecast + accuracy measures using the lpacf method and forecast +accuracy measures using the ARMA method.

Author(s)

Rebecca Killick

References

Killick, R., Knight, M.I., Nason, G.P., Nunes M.A., Eckley I.A. (2023) Automatic Locally Stationary Time Series Forecasting with application to predicting U.K. Gross Value Added Time Series under sudden shocks caused by the COVID pandemic arXiv:2303.07772

See Also

lpacf.plot, forecastlpacf

Examples

## Not run: 
	data(windanomaly)
	out=analyze.windanomaly()

## End(Not run)

forecastLSW documentation built on Aug. 22, 2025, 1:09 a.m.