# Time Series for Data Science

## Documentation for package ‘tswge’ version 2.1.0

- DESCRIPTION file.

## Help Pages

A B C D E F G H I K L M N O P R S T U W Y

tswge-package | Time Series package for Woodward, Gray, and Elliott text

## -- A --

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

## -- B --

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
Bsales | Toy Data Set of Business Sales Data
bumps16 | 16 point bumps signal
bumps256 | 256 point bumps signal
butterworth.wge | Perform Butterworth Filter

## -- C --

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

## -- D --

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

## -- E --

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

## -- F --

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

## -- G --

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

## -- H --

hadley | Global temperature data
hilbert.wge | Function to calculate the Hilbert transformation of a given real valued signal(even length)

## -- I --

is.glambda.wge | Instantaneous spectrum
is.sample.wge | Sample instantaneous spectrum based on periodogram

## -- K --

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

## -- L --

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

## -- M --

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
MedDays | Median days a house stayed on the market
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

## -- N --

NAICS | Monthly Retail Sales Data
nbumps256 | 256 noisy bumps signal
nile.min | Annual minimal water levels of Nile river
noctula | Nyctalus noctula echolocation data
NSA | Monthly Total Vehicle Sales

## -- O --

ozona | Daily Number of Chicken-Fried Steaks Sold

## -- P --

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

## -- R --

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

## -- S --

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

## -- T --

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 | Time Series package for Woodward, Gray, and Elliott text
tx.unemp.adj | Texas Seasonally Adjusted Unnemployment Rates
tx.unemp.unadj | Texas Unadjusted Unnemployment Rates

## -- U --

unit.circle.wge | Plot the roots of the characteristic equation on the complex plain.
us.retail | Quarterly US Retail Sales
uspop | US population

## -- W --

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

## -- Y --

yellowcab.precleaned | Precleaned Yellow Cab data