analyze.abmld2 {forecastLSW}

Analyzes the abmld2 data, see below for more details.

Description

Takes the abmld2 data and analyzes it.

Usage

analyze.abmld2(h=10,atTime=NULL,atLag=NULL)

Arguments

h

Numeric value for a 1: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 abmld2 is 147 and thus the plausible values are [74,147].

atLag

Vector of the lags (columns) of the lpacf to be plotted. The default maximum lag is floor(10 * log10(n)) which is 23 for abmld2.

Details

Takes the abmld2 data and analyzes it. Specifically the following is produced:

• time series plot of the abmld2 data

• the lpacf for the abmld2 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(abmld2)
	out=analyze.abmld2()

## End(Not run)