Bsales                  Toy Data Set of Business Sales Data
MedDays                 Median days a house stayed on the market
NAICS                   Monthly Retail Sales Data
NSA                     Monthly Total Vehicle Sales
aic.ar.wge              AR Model Identification for AR models
aic.burg.wge            AR Model Identification using Burg Estimates
aic.wge                 ARMA Model Identification
aic5.ar.wge             Return top 5 AIC, AICC, or BIC picks for AR
                        model fits
aic5.wge                Return top 5 AIC, AICC, or BIC picks
airline                 Classical Airline Passenger Data
airlog                  Natural log of airline data
appy                    Non-perforated appendicitis data shown in
                        Figure 10.8 (solid line) in Applied Time Series
                        Analysis with R, second edition by Woodward,
                        Gray, and Elliott
artrans.wge             Perform Ar transformations
backcast.wge            Calculate backcast residuals
bat                     Bat echolocation signal shown in Figure 13.11a
                        in Applied Time Series Analysis with R, second
                        edition by Woodward, Gray, and Elliott
bitcoin                 Daily Bitcoin Prices From May 1, 2020 to April
                        30, 2021
bumps16                 16 point bumps signal
bumps256                256 point bumps signal
butterworth.wge         Perform Butterworth Filter
cardiac                 Weekly Cardiac Mortality Data
cement                  Cement data shown in Figure 3.30a in Applied
                        Time Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
chirp                   Chirp data shown in Figure 12.2a in Applied
                        Time Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
co.wge                  Cochrane-Orcutt test for trend
dfw.2011                DFW Monthly Temperatures from January 2011
                        through December 2020
dfw.mon                 DFW Monthly Temperatures
dfw.yr                  DFW Annual Temperatures
doppler                 Doppler Data
doppler2                Doppler signal in Figure 13.10 in Applied Time
                        Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
dow.annual              DOW Annual Closing Averages
dow.rate                DOW Daily Rate of Return Data
dow1000                 Dow Jones daily rate of return data for 1000
                        days
dow1985                 Daily DOW Closing Prices 1985 through 2020
dowjones2014            Dow Jones daily averages for 2014
eco.cd6                 6-month rates
eco.corp.bond           Corporate bond rates
eco.mort30              30 year mortgage rates
est.ar.wge              Estimate parameters of an AR(p) model
est.arma.wge            Function to calculate ML estimates of
                        parameters of stationary ARMA models
est.farma.wge           Estimate the parameters of a FARMA model.
est.garma.wge           Estimate the parameters of a GARMA model.
est.glambda.wge         Estimate the value of lambda and offset to
                        produce a stationary dual.
expsmooth.wge           Exponential Smoothing
factor.comp.wge         Create a factor table and AR components for an
                        AR realization
factor.wge              Produce factor table for a kth order AR or MA
                        model
fig1.10a                Simulated data shown in Figure 1.10a in Applied
                        Time Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
fig1.10b                Simulated data shown in Figure 1.10b in Applied
                        Time Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
fig1.10c                Simulated data in Figure 1.10c in Applied Time
                        Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
fig1.10d                Simulated data in Figure 1.10d in Applied Time
                        Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
fig1.16a                Simulated data for Figure 1.16a in Applied Time
                        Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
fig1.21a                Simulated shown in Figure 1.21a of Woodward,
                        Gray, and Elliott text
fig1.22a                White noise data
fig1.5                  Simulated data shown in Figure 1.5 in Applied
                        Time Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
fig10.11x               Simulated data shown in Figure 10.11 (solid
                        line) in Applied Time Series Analysis with R,
                        second edition by Woodward, Gray, and Elliott
fig10.11y               Simulated data shown in Figure 10.11 (dashed
                        line) in Applied Time Series Analysis with R,
                        second edition by Woodward, Gray, and Elliott
fig10.1bond             Data for Figure 10.1b in Applied Time Series
                        Analysis with R, second edition by Woodward,
                        Gray, and Elliott
fig10.1cd               Data shown in Figure 10.1a in Applied Time
                        Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
fig10.1mort             Data shown in Figure 10.1c in Applied Time
                        Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
fig10.3x1               Variable X1 for the bivariate realization shown
                        in Figure 10.3"
fig10.3x2               Variable X2 for the bivariate realization shown
                        in Figure 10.3"
fig11.12                Data shown in Figure 11.12a in Applied Time
                        Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
fig11.4a                Data shown in Figure 11.4a in Applied Time
                        Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
fig12.1a                Simulated data with two frequencies shown in
                        Figure 12.1a in Applied Time Series Analysis
                        with R, second edition by Woodward, Gray, and
                        Elliott
fig12.1b                Simulated data with two frequencies shown in
                        Figure 12.1b in Applied Time Series Analysis
                        with R, second edition by Woodward, Gray, and
                        Elliott
fig13.18a               Simulated data shown in Figure 3.18a in Applied
                        Time Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
fig13.2c                TVF data shown in Figure 13.2c in Applied Time
                        Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
fig3.10d                AR(2) Realization (1-.95)^2X(t)=a(t)
fig3.16a                Figure 3.16a in "Applied Time Series Analysis
                        with R, 2nd edition" by Woodward, Gray, and
                        Elliott
fig3.18a                Figure 3.18a in "Applied Time Series Analysis
                        with R, 2nd edition" by Woodward, Gray, and
                        Elliott
fig3.24a                ARMA(2,1) realization
fig3.29a                Simulated data shown in Figure 3.29a in Applied
                        Time Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
fig4.8a                 Gaussian White Noise
fig5.3c                 Data from Figure 5.3c in "Applied Time Series
                        Analysis with R, 2nd edition" by Woodward,
                        Gray, and Elliott
fig6.11a                Cyclical Data
fig6.1nf                Data in Figure 6.1 without the forecasts
fig6.2nf                Data in Figure 6.2 without the forecasts
fig6.5nf                Data in Figure 6.5 without the forecasts
fig6.6nf                Data in Figure 6.6 without the forecasts
fig6.7nf                Data in Figure 6.2 without the forecasts
fig6.8nf                Simulated seasonal data with s=12
fig8.11a                Data for Figure 8.11a in Applied Time Series
                        Analysis with R, second edition by Woodward,
                        Gray, and Elliott
fig8.4a                 Data for Figure 8.4a in Applied time series
                        Analysis with R, second edition by Woodward,
                        Gray, and Elliott
fig8.6a                 Data for Figure 8.6a in Applied time series
                        Analysis with R, second edition by Woodward,
                        Gray, and Elliott
fig8.8a                 Data for Figure 8.8a in Applied time series
                        Analysis with R, second edition by Woodward,
                        Gray, and Elliott
flu                     Influenza data shown in Figure 10.8 (dotted
                        line)
fore.arima.wge          Function for forecasting from known model which
                        may have (1-B)^d and/or seasonal factors
fore.arma.wge           Forecast from known model
fore.aruma.wge          Function for forecasting from known model which
                        may have (1-B)^d, seasonal, and/or other
                        nonstationary factors
fore.farma.wge          Forecast using a FARMA model
fore.garma.wge          Forecast using a GARMA model
fore.glambda.wge        Forecast using a G(lambda) model
fore.sigplusnoise.wge   Forecasting signal plus noise models
freeze                  Minimum temperature data
freight                 Freight data
gegenb.wge              Calculates Gegenbauer polynomials
gen.arch.wge            Generate a realization from an ARCH(q0) model
gen.arima.wge           Function to generate an ARIMA (or ARMA)
                        realization
gen.arma.wge            Function to generate an ARMA realization
gen.aruma.wge           Function to generate an ARUMA (or ARMA or
                        ARIMA) realization
gen.garch.wge           Generate a realization from a GARCH(p0,q0)
                        model
gen.garma.wge           Function to generate a GARMA realization
gen.geg.wge             Function to generate a Gegenbauer realization
gen.glambda.wge         Function to generate a g(lambda) realization
gen.sigplusnoise.wge    Generate data from a signal-plus-noise model
global.temp             Global Temperature Data: 1850-2009
global2020              Global Temperature Data: 1880-2009
hadley                  Global temperature data
hilbert.wge             Function to calculate the Hilbert
                        transformation of a given real valued
                        signal(even length)
is.glambda.wge          Instantaneous spectrum
is.sample.wge           Sample instantaneous spectrum based on
                        periodogram
kalman.miss.wge         Kalman filter for simple signal plus noise
                        model with missing data
kalman.wge              Kalman filter for simple signal plus noise
                        model
kingkong                King Kong Eats Grass
lavon                   Lavon lake water levels
lavon15                 Lavon Lake Levels to September 30, 2015
linearchirp             Linear chirp data.
ljung.wge               Ljung-Box Test
llynx                   Log (base 10) of lynx data
lynx                    Lynx data
ma.pred.wge             Predictive or rolling moving average
ma.smooth.wge           Centered Moving Average Smoother
ma2.table7.1            Simulated MA(2) data
macoef.geg.wge          Calculate coefficients of the general linear
                        process form of a Gegenbauer process
mass.mountain           Massachusettts Mountain Earthquake Data
mm.eq                   Massachusetts Mountain Earthquake data shown in
                        Figure 13.13a in Applied Time Series Analysis
                        with R, second edition by Woodward, Gray, and
                        Elliott
mult.wge                Multiply Factors
nbumps256               256 noisy bumps signal
nile.min                Annual minimal water levels of Nile river
noctula                 Nyctalus noctula echolocation data
ozona                   Daily Number of Chicken-Fried Steaks Sold
pacfts.wge              Compute partial autocorrelations
parzen.wge              Smoothed Periodogram using Parzen Window
patemp                  Pennsylvania average monthly temperatures
period.wge              Calculate the periodogram
pi.weights.wge          Calculate pi weights for an ARMA model
plotts.dwt.wge          Plots Discrete Wavelet Transform (DWT)
plotts.mra.wge          Plots MRA plot)
plotts.parzen.wge       Calculate and plot the periodogram and Parzen
                        window estimates with differing trunctaion
                        points
plotts.sample.wge       Plot Data, Sample Autocorrelations,
                        Periodogram, and Parzen Spectral Estimate
plotts.true.wge         Plot of generated data, true autocorrelations
                        and true spectral density for ARMA model
plotts.wge              Plot a time series realization
prob10.4                Data matrix for Problem 10.4 in "Applied Time
                        Series Analysis with R, 2nd edition" by
                        Woodward, Gray, and Elliott
prob10.6x               Data for Problem 10.6 in Applied Time Series
                        Analysis with R, second edition by Woodward,
                        Gray, and Elliott
prob10.6y               Simulated observed data for Problem 10.6 in
                        Applied Time Series Analysis with R, second
                        edition by Woodward, Gray, and Elliott
prob10.7x               Data for Problem 10.7 in Applied Time Series
                        Analysis with R, second edition by Woodward,
                        Gray, and Elliott
prob10.7y               Simulated observed data for Problem 10.6 in
                        Applied Time Series Analysis with R, second
                        edition by Woodward, Gray, and Elliott
prob11.5                Data for Problem 11.5 in Applied Time Series
                        Analysis with R, second edition by Woodward,
                        Gray, and Elliott
prob12.1c               Data for Problem 12.1c and 12.3c in Applied
                        Time Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
prob12.3a               Data for Problem 12.3a in Applied Time Series
                        Analysis with R, second edition by Woodward,
                        Gray, and Elliott
prob12.3b               Data for Problem 12.3b in Applied Time Series
                        Analysis with R, second edition by Woodward,
                        Gray, and Elliott
prob12.6c               Data set for Problem 12.6(C) in Applied Time
                        Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
prob13.2                Data for Problem 13.2 in Applied Time Series
                        Analysis with R, second edition by Woodward,
                        Gray, and Elliott
prob8.1a                Data for Problem 8.1 in "Applied Time Series
                        Analysis with R, 2nd edition" by Woodward,
                        Gray, and Elliott
prob8.1b                Data for Problem 8.1 in "Applied Time Series
                        Analysis with R, 2nd edition" by Woodward,
                        Gray, and Elliott
prob8.1c                Data for Problem 8.1 in "Applied Time Series
                        Analysis with R, 2nd edition" by Woodward,
                        Gray, and Elliott
prob8.1d                Data for Problem 8.1 in "Applied Time Series
                        Analysis with R, 2nd edition" by Woodward,
                        Gray, and Elliott
prob9.6c1               Data set 1 for Problem 6.1c
prob9.6c2               Data set 2 for Problem 6.1c
prob9.6c3               Data set 3 for Problem 6.1c
prob9.6c4               Data set 4 for Problem 6.1c
psi.weights.wge         Calculate psi weights for an ARMA model
rate                    Daily DOW rate of Return
roll.win.rmse.nn.wge    Function to Calculate the Rolling Window RMSE
roll.win.rmse.wge       Function to Calculate the Rolling Window RMSE
sample.spec.wge         Smoothed Periodogram using Parzen Window
slr.wge                 Simple Linear Regression
ss08                    Sunspot Data
ss08.1850               Sunspot data from 1850 through 2008 for
                        matching with global temperature data (hadley)
starwort.ex             Starwort Explosion data shown in Figure 13.13a
                        in Applied Time Series Analysis with R, second
                        edition by Woodward, Gray, and Elliott
sunspot.classic         Classic Sunspot Data: 1749-1924
sunspot2.0              Annual Sunspot2.0 Numbers
sunspot2.0.month        Monthly Sunspot2.0 Numbers
table10.1.noise         Noise related to data set, the first 5 points
                        of which are shown in Table 10.1 in Applied
                        Time Series Analysis with R, second edition by
                        Woodward, Gray, and Elliott
table10.1.signal        Underlying, unobservable signal (X(t), the
                        first 5 points of which are shown in Table 10.1
                        in Applied Time Series Analysis with R, second
                        edition by Woodward, Gray, and Elliott
table7.1                MA(2) data for Table 7.1
tesla                   Tesla Stock Prices
trans.to.dual.wge       Transforms TVF data set to a dual data set
trans.to.original.wge   Transforms dual data set back to original time
                        scale
true.arma.aut.wge       True ARMA autocorrelations
true.arma.spec.wge      True ARMA Spectral Density
true.farma.aut.wge      True FARMA autocorrelations
true.garma.aut.wge      True GARMA autocorrelations
tswge-package           Time Series package for Woodward, Gray, and
                        Elliott text
tx.unemp.adj            Texas Seasonally Adjusted Unnemployment Rates
tx.unemp.unadj          Texas Unadjusted Unnemployment Rates
unit.circle.wge         Plot the roots of the characteristic equation
                        on the complex plain.
us.retail               Quarterly US Retail Sales
uspop                   US population
wages                   Daily wages in Pounds from 1260 to 1944 for
                        England
wbg.boot.wge            Woodward-Bottone-Gray test for trend
whale                   Whale click data
wtcrude                 West Texas Intermediate Crude Oil Prices
wtcrude2020             Monthly WTI Crude Oil Prices
wv.wge                  Function to calculate Wigner Ville spectrum
yellowcab.precleaned    Precleaned Yellow Cab data
