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tswge (version 2.1.0)

Time Series for Data Science

Description

Accompanies the texts Time Series for Data Science with R by Woodward, Sadler and Robertson & Applied Time Series Analysis with R, 2nd edition by Woodward, Gray, and Elliott. It is helpful for data analysis and for time series instruction.

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Version

Install

install.packages('tswge')

Monthly Downloads

708

Version

2.1.0

License

GPL-2

Maintainer

Bivin Sadler

Last Published

January 31st, 2023

Functions in tswge (2.1.0)

aic.burg.wge

AR Model Identification using Burg Estimates
cardiac

Weekly Cardiac Mortality Data
butterworth.wge

Perform Butterworth Filter
aic5.wge

Return top 5 AIC, AICC, or BIC picks
aic.wge

ARMA Model Identification
dfw.mon

DFW Monthly Temperatures
airline

Classical Airline Passenger Data
MedDays

Median days a house stayed on the market
NAICS

Monthly Retail Sales Data
aic5.ar.wge

Return top 5 AIC, AICC, or BIC picks for AR model fits
NSA

Monthly Total Vehicle Sales
artrans.wge

Perform Ar transformations
airlog

Natural log of airline data
bat

Bat echolocation signal shown in Figure 13.11a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
bumps16

16 point bumps signal
co.wge

Cochrane-Orcutt test for trend
bitcoin

Daily Bitcoin Prices From May 1, 2020 to April 30, 2021
bumps256

256 point bumps signal
fig1.16a

Simulated data for Figure 1.16a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
doppler

Doppler Data
fig1.21a

Simulated shown in Figure 1.21a of Woodward, Gray, and Elliott text
dowjones2014

Dow Jones daily averages for 2014
doppler2

Doppler signal in Figure 13.10 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
dfw.2011

DFW Monthly Temperatures from January 2011 through December 2020
dfw.yr

DFW Annual Temperatures
est.farma.wge

Estimate the parameters of a FARMA model.
backcast.wge

Calculate backcast residuals
dow.annual

DOW Annual Closing Averages
dow1000

Dow Jones daily rate of return data for 1000 days
eco.cd6

6-month rates
dow1985

Daily DOW Closing Prices 1985 through 2020
fig1.10c

Simulated data in Figure 1.10c in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
est.glambda.wge

Estimate the value of lambda and offset to produce a stationary dual.
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
expsmooth.wge

Exponential Smoothing
dow.rate

DOW Daily Rate of Return Data
fig10.3x2

Variable X2 for the bivariate realization shown in Figure 10.3"
cement

Cement data shown in Figure 3.30a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
est.ar.wge

Estimate parameters of an AR(p) model
eco.corp.bond

Corporate bond rates
fig10.1mort

Data shown in Figure 10.1c 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
est.arma.wge

Function to calculate ML estimates of parameters of stationary ARMA models
fig11.12

Data shown in Figure 11.12a 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"
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
fig1.10d

Simulated data in Figure 1.10d in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
fig3.16a

Figure 3.16a in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott
fig3.24a

ARMA(2,1) realization
fig3.18a

Figure 3.18a in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott
eco.mort30

30 year mortgage rates
fig4.8a

Gaussian White Noise
fig11.4a

Data shown in Figure 11.4a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
fig5.3c

Data from Figure 5.3c in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott
fig8.11a

Data for Figure 8.11a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
fig6.8nf

Simulated seasonal data with s=12
fore.arima.wge

Function for forecasting from known model which may have (1-B)^d and/or seasonal factors
fig3.29a

Simulated data shown in Figure 3.29a 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
fig6.11a

Cyclical Data
fig1.10a

Simulated data shown in Figure 1.10a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
fig6.6nf

Data in Figure 6.6 without the forecasts
gen.arma.wge

Function to generate an ARMA realization
fig1.10b

Simulated data shown in Figure 1.10b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
fig6.1nf

Data in Figure 6.1 without the forecasts
factor.comp.wge

Create a factor table and AR components for an AR realization
est.garma.wge

Estimate the parameters of a GARMA model.
fore.arma.wge

Forecast from known model
kalman.wge

Kalman filter for simple signal plus noise model
gen.aruma.wge

Function to generate an ARUMA (or ARMA or ARIMA) realization
hilbert.wge

Function to calculate the Hilbert transformation of a given real valued signal(even length)
factor.wge

Produce factor table for a kth order AR or MA model
fore.garma.wge

Forecast using a GARMA model
is.glambda.wge

Instantaneous spectrum
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
fore.glambda.wge

Forecast using a G(lambda) model
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
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
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)
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)
freight

Freight data
lavon

Lavon lake water levels
gegenb.wge

Calculates Gegenbauer polynomials
is.sample.wge

Sample instantaneous spectrum based on periodogram
kingkong

King Kong Eats Grass
kalman.miss.wge

Kalman filter for simple signal plus noise model with missing data
fore.sigplusnoise.wge

Forecasting signal plus noise models
freeze

Minimum temperature data
lavon15

Lavon Lake Levels to September 30, 2015
gen.geg.wge

Function to generate a Gegenbauer realization
fig6.7nf

Data in Figure 6.2 without the forecasts
ma2.table7.1

Simulated MA(2) data
gen.glambda.wge

Function to generate a g(lambda) realization
llynx

Log (base 10) of lynx data
lynx

Lynx data
ma.pred.wge

Predictive or rolling moving average
nile.min

Annual minimal water levels of Nile river
macoef.geg.wge

Calculate coefficients of the general linear process form of a Gegenbauer process
ma.smooth.wge

Centered Moving Average Smoother
fore.aruma.wge

Function for forecasting from known model which may have (1-B)^d, seasonal, and/or other nonstationary factors
prob12.1c

Data for Problem 12.1c and 12.3c in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
fore.farma.wge

Forecast using a FARMA model
mass.mountain

Massachusettts Mountain Earthquake Data
fig13.18a

Simulated data shown in Figure 3.18a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
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
plotts.true.wge

Plot of generated data, true autocorrelations and true spectral density for ARMA model
plotts.wge

Plot a time series realization
noctula

Nyctalus noctula echolocation data
prob10.4

Data matrix for Problem 10.4 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott
mult.wge

Multiply Factors
nbumps256

256 noisy bumps signal
plotts.dwt.wge

Plots Discrete Wavelet Transform (DWT)
gen.garch.wge

Generate a realization from a GARCH(p0,q0) model
plotts.parzen.wge

Calculate and plot the periodogram and Parzen window estimates with differing trunctaion points
plotts.mra.wge

Plots MRA plot)
prob10.6x

Data for Problem 10.6 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
plotts.sample.wge

Plot Data, Sample Autocorrelations, Periodogram, and Parzen Spectral Estimate
prob10.7y

Simulated observed data for Problem 10.6 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
fig6.2nf

Data in Figure 6.2 without the forecasts
fig6.5nf

Data in Figure 6.5 without the forecasts
prob12.3b

Data for Problem 12.3b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
prob9.6c2

Data set 2 for Problem 6.1c
prob9.6c3

Data set 3 for Problem 6.1c
prob11.5

Data for Problem 11.5 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
ss08

Sunspot Data
starwort.ex

Starwort Explosion data shown in Figure 13.13a 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
gen.garma.wge

Function to generate a GARMA realization
gen.arch.wge

Generate a realization from an ARCH(q0) model
fig8.6a

Data for Figure 8.6a 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
tesla

Tesla Stock Prices
prob8.1d

Data for Problem 8.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott
roll.win.rmse.wge

Function to Calculate the Rolling Window RMSE
ample.spec.wge

Smoothed Periodogram using Parzen Window
trans.to.dual.wge

Transforms TVF data set to a dual data set
prob12.3a

Data for Problem 12.3a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
true.garma.aut.wge

True GARMA autocorrelations
sunspot2.0.month

Monthly Sunspot2.0 Numbers
tswge-package

Time Series package for Woodward, Gray, and Elliott text
gen.arima.wge

Function to generate an ARIMA (or ARMA) realization
global2020

Global Temperature Data: 1880-2009
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
prob13.2

Data for Problem 13.2 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
hadley

Global temperature data
ozona

Daily Number of Chicken-Fried Steaks Sold
gen.sigplusnoise.wge

Generate data from a signal-plus-noise model
prob8.1a

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
global.temp

Global Temperature Data: 1850-2009
linearchirp

Linear chirp data.
slr.wge

Simple Linear Regression
ss08.1850

Sunspot data from 1850 through 2008 for matching with global temperature data (hadley)
pacfts.wge

Compute partial autocorrelations
parzen.wge

Smoothed Periodogram using Parzen Window
patemp

Pennsylvania average monthly temperatures
tx.unemp.adj

Texas Seasonally Adjusted Unnemployment Rates
prob10.6y

Simulated observed data for Problem 10.6 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
trans.to.original.wge

Transforms dual data set back to original time scale
ljung.wge

Ljung-Box Test
tx.unemp.unadj

Texas Unadjusted Unnemployment Rates
period.wge

Calculate the periodogram
true.arma.aut.wge

True ARMA autocorrelations
prob10.7x

Data for Problem 10.7 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
prob9.6c4

Data set 4 for Problem 6.1c
pi.weights.wge

Calculate pi weights for an ARMA model
prob8.1b

Data for Problem 8.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott
psi.weights.wge

Calculate psi weights for an ARMA model
prob8.1c

Data for Problem 8.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott
sunspot.classic

Classic Sunspot Data: 1749-1924
rate

Daily DOW rate of Return
sunspot2.0

Annual Sunspot2.0 Numbers
true.arma.spec.wge

True ARMA Spectral Density
true.farma.aut.wge

True FARMA autocorrelations
wbg.boot.wge

Woodward-Bottone-Gray test for trend
unit.circle.wge

Plot the roots of the characteristic equation on the complex plain.
roll.win.rmse.nn.wge

Function to Calculate the Rolling Window RMSE
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
us.retail

Quarterly US Retail Sales
table7.1

MA(2) data for Table 7.1
wtcrude

West Texas Intermediate Crude Oil Prices
uspop

US population
wages

Daily wages in Pounds from 1260 to 1944 for England
whale

Whale click data
wv.wge

Function to calculate Wigner Ville spectrum
yellowcab.precleaned

Precleaned Yellow Cab data
wtcrude2020

Monthly WTI Crude Oil Prices
aic.ar.wge

AR Model Identification for AR models
Bsales

Toy Data Set of Business Sales Data