Package: tswge 2.1.0

tswge: Time Series for Data Science

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.

Authors:Wayne Woodward

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# Install 'tswge' in R:
install.packages('tswge', repos = c('https://bsnatr.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • freeze - Minimum temperature data
  • freight - Freight data
  • global.temp - Global Temperature Data: 1850-2009
  • global2020 - Global Temperature Data: 1880-2009
  • hadley - Global temperature data
  • kingkong - King Kong Eats Grass
  • lavon - Lavon lake water levels
  • lavon15 - Lavon Lake Levels to September 30, 2015
  • linearchirp - Linear chirp data.
  • llynx - Log (base 10) of lynx data
  • lynx - Lynx data
  • ma2.table7.1 - Simulated MA(2) data
  • 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
  • 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
  • patemp - Pennsylvania average monthly temperatures
  • 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
  • rate - Daily DOW rate of Return
  • ss08 - Sunspot Data
  • ss08.1850 - Sunspot data from 1850 through 2008 for matching with global temperature 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
  • 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
  • tx.unemp.adj - Texas Seasonally Adjusted Unnemployment Rates
  • tx.unemp.unadj - Texas Unadjusted Unnemployment Rates
  • us.retail - Quarterly US Retail Sales
  • uspop - US population
  • wages - Daily wages in Pounds from 1260 to 1944 for England
  • whale - Whale click data
  • wtcrude - West Texas Intermediate Crude Oil Prices
  • wtcrude2020 - Monthly WTI Crude Oil Prices
  • yellowcab.precleaned - Precleaned Yellow Cab data

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.67 score 472 scripts 289 downloads 68 exports 146 dependencies

Last updated 2 years agofrom:435566d44f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-winOKOct 30 2024
R-4.5-linuxOKOct 30 2024
R-4.4-winOKOct 30 2024
R-4.4-macOKOct 30 2024
R-4.3-winOKOct 30 2024
R-4.3-macOKOct 30 2024

Exports:aic.ar.wgeaic.burg.wgeaic.wgeaic5.ar.wgeaic5.wgeartrans.wgebackcast.wgebutterworth.wgeco.wgeest.ar.wgeest.arma.wgeest.farma.wgeest.garma.wgeest.glambda.wgeexpsmooth.wgefactor.comp.wgefactor.wgefore.arima.wgefore.arma.wgefore.aruma.wgefore.farma.wgefore.garma.wgefore.glambda.wgefore.sigplusnoise.wgegegenb.wgegen.arch.wgegen.arima.wgegen.arma.wgegen.aruma.wgegen.garch.wgegen.garma.wgegen.geg.wgegen.glambda.wgegen.sigplusnoise.wgehilbert.wgeis.glambda.wgeis.sample.wgekalman.miss.wgekalman.wgeljung.wgema.pred.wgema.smooth.wgemacoef.geg.wgemult.wgepacfts.wgeparzen.wgeperiod.wgepi.weights.wgeplotts.dwt.wgeplotts.mra.wgeplotts.parzen.wgeplotts.sample.wgeplotts.true.wgeplotts.wgepsi.weights.wgeroll.win.rmse.nn.wgeroll.win.rmse.wgesample.spec.wgeslr.wgetrans.to.dual.wgetrans.to.original.wgetrue.arma.aut.wgetrue.arma.spec.wgetrue.farma.aut.wgetrue.garma.aut.wgeunit.circle.wgewbg.boot.wgewv.wge

Dependencies:askpassastsabackportsbase64encbitbit64blobbroombslibcachemcallrcellrangerclicliprcodetoolscolorspaceconflictedcpp11crayoncurldata.tableDBIdbplyrDerivdigestdplyrdtplyrevaluatefansifarverfastmapfontawesomeforcatsforeachforecastfracdifffsgarglegenericsggplot2glmnetgluegoogledrivegooglesheets4greyboxgtablehavenhighrhmshtmltoolshttridsisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclelmtestlubridatemagrittrMAPAMASSMatrixmemoisemgcvmimemodelrmultitapermunsellneuralnetnlmenloptrnnetnnforopensslpillarpkgconfigplotrixPolynomFpracmaprettyunitsprocessxprogresspspurrrquadprogquantmodR6raggrappdirsRColorBrewerRcppRcppArmadilloRcppEigenreadrreadxlrematchrematch2reprexrlangrmarkdownrstudioapirvestsassscalesselectrshapesignalsmoothstatmodstringistringrsurvivalsyssystemfontstexregtextshapingtibbletidyrtidyselecttidyversetimechangetimeDatetinytextseriestsutilsTTRtzdburcaurootutf8uuidvctrsviridisLitevroomwaveslimwithrxfunxml2xtablextsyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Time Series package for Woodward, Gray, and Elliott texttswge-package tswge
AR Model Identification for AR modelsaic.ar.wge
AR Model Identification using Burg Estimatesaic.burg.wge
ARMA Model Identificationaic.wge
Return top 5 AIC, AICC, or BIC picks for AR model fitsaic5.ar.wge
Return top 5 AIC, AICC, or BIC picksaic5.wge
Classical Airline Passenger Dataairline
Natural log of airline dataairlog
Smoothed Periodogram using Parzen Windowsample.spec.wge
Non-perforated appendicitis data shown in Figure 10.8 (solid line) in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottappy
Perform Ar transformationsartrans.wge
Calculate backcast residualsbackcast.wge
Bat echolocation signal shown in Figure 13.11a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottbat
Daily Bitcoin Prices From May 1, 2020 to April 30, 2021bitcoin
Toy Data Set of Business Sales DataBsales
16 point bumps signalbumps16
256 point bumps signalbumps256
Perform Butterworth Filterbutterworth.wge
Weekly Cardiac Mortality Datacardiac
Cement data shown in Figure 3.30a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottcement
Chirp data shown in Figure 12.2a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottchirp
Cochrane-Orcutt test for trendco.wge
DFW Monthly Temperatures from January 2011 through December 2020dfw.2011
DFW Monthly Temperaturesdfw.mon
DFW Annual Temperaturesdfw.yr
Doppler Datadoppler
Doppler signal in Figure 13.10 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottdoppler2
DOW Annual Closing Averagesdow.annual
DOW Daily Rate of Return Datadow.rate
Dow Jones daily rate of return data for 1000 daysdow1000
Daily DOW Closing Prices 1985 through 2020dow1985
Dow Jones daily averages for 2014dowjones2014
6-month rateseco.cd6
Corporate bond rateseco.corp.bond
30 year mortgage rateseco.mort30
Estimate parameters of an AR(p) modelest.ar.wge
Function to calculate ML estimates of parameters of stationary ARMA modelsest.arma.wge
Estimate the parameters of a FARMA model.est.farma.wge
Estimate the parameters of a GARMA model.est.garma.wge
Estimate the value of lambda and offset to produce a stationary dual.est.glambda.wge
Exponential Smoothingexpsmooth.wge
Create a factor table and AR components for an AR realizationfactor.comp.wge
Produce factor table for a kth order AR or MA modelfactor.wge
Simulated data shown in Figure 1.10a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig1.10a
Simulated data shown in Figure 1.10b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig1.10b
Simulated data in Figure 1.10c in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig1.10c
Simulated data in Figure 1.10d in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig1.10d
Simulated data for Figure 1.16a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig1.16a
Simulated shown in Figure 1.21a of Woodward, Gray, and Elliott textfig1.21a
White noise datafig1.22a
Simulated data shown in Figure 1.5 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig1.5
Simulated data shown in Figure 10.11 (solid line) in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig10.11x
Simulated data shown in Figure 10.11 (dashed line) in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig10.11y
Data for Figure 10.1b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig10.1bond
Data shown in Figure 10.1a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig10.1cd
Data shown in Figure 10.1c in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig10.1mort
Variable X1 for the bivariate realization shown in Figure 10.3"fig10.3x1
Variable X2 for the bivariate realization shown in Figure 10.3"fig10.3x2
Data shown in Figure 11.12a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig11.12
Data shown in Figure 11.4a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig11.4a
Simulated data with two frequencies shown in Figure 12.1a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig12.1a
Simulated data with two frequencies shown in Figure 12.1b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig12.1b
Simulated data shown in Figure 3.18a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig13.18a
TVF data shown in Figure 13.2c in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig13.2c
AR(2) Realization (1-.95)^2X(t)=a(t)fig3.10d
Figure 3.16a in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliottfig3.16a
Figure 3.18a in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliottfig3.18a
ARMA(2,1) realizationfig3.24a
Simulated data shown in Figure 3.29a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig3.29a
Gaussian White Noisefig4.8a
Data from Figure 5.3c in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliottfig5.3c
Cyclical Datafig6.11a
Data in Figure 6.1 without the forecastsfig6.1nf
Data in Figure 6.2 without the forecastsfig6.2nf
Data in Figure 6.5 without the forecastsfig6.5nf
Data in Figure 6.6 without the forecastsfig6.6nf
Data in Figure 6.2 without the forecastsfig6.7nf
Simulated seasonal data with s=12fig6.8nf
Data for Figure 8.11a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottfig8.11a
Data for Figure 8.4a in Applied time series Analysis with R, second edition by Woodward, Gray, and Elliottfig8.4a
Data for Figure 8.6a in Applied time series Analysis with R, second edition by Woodward, Gray, and Elliottfig8.6a
Data for Figure 8.8a in Applied time series Analysis with R, second edition by Woodward, Gray, and Elliottfig8.8a
Influenza data shown in Figure 10.8 (dotted line)flu
Function for forecasting from known model which may have (1-B)^d and/or seasonal factorsfore.arima.wge
Forecast from known modelfore.arma.wge
Function for forecasting from known model which may have (1-B)^d, seasonal, and/or other nonstationary factorsfore.aruma.wge
Forecast using a FARMA modelfore.farma.wge
Forecast using a GARMA modelfore.garma.wge
Forecast using a G(lambda) modelfore.glambda.wge
Forecasting signal plus noise modelsfore.sigplusnoise.wge
Minimum temperature datafreeze
Freight datafreight
Calculates Gegenbauer polynomialsgegenb.wge
Generate a realization from an ARCH(q0) modelgen.arch.wge
Function to generate an ARIMA (or ARMA) realizationgen.arima.wge
Function to generate an ARMA realizationgen.arma.wge
Function to generate an ARUMA (or ARMA or ARIMA) realizationgen.aruma.wge
Generate a realization from a GARCH(p0,q0) modelgen.garch.wge
Function to generate a GARMA realizationgen.garma.wge
Function to generate a Gegenbauer realizationgen.geg.wge
Function to generate a g(lambda) realizationgen.glambda.wge
Generate data from a signal-plus-noise modelgen.sigplusnoise.wge
Global Temperature Data: 1850-2009global.temp
Global Temperature Data: 1880-2009global2020
Global temperature datahadley
Function to calculate the Hilbert transformation of a given real valued signal(even length)hilbert.wge
Instantaneous spectrumis.glambda.wge
Sample instantaneous spectrum based on periodogramis.sample.wge
Kalman filter for simple signal plus noise model with missing datakalman.miss.wge
Kalman filter for simple signal plus noise modelkalman.wge
King Kong Eats Grasskingkong
Lavon lake water levelslavon
Lavon Lake Levels to September 30, 2015lavon15
Linear chirp data.linearchirp
Ljung-Box Testljung.wge
Log (base 10) of lynx datallynx
Lynx datalynx
Predictive or rolling moving averagema.pred.wge
Centered Moving Average Smootherma.smooth.wge
Simulated MA(2) datama2.table7.1
Calculate coefficients of the general linear process form of a Gegenbauer processmacoef.geg.wge
Massachusettts Mountain Earthquake Datamass.mountain
Median days a house stayed on the marketMedDays
Massachusetts Mountain Earthquake data shown in Figure 13.13a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottmm.eq
Multiply Factorsmult.wge
Monthly Retail Sales DataNAICS
256 noisy bumps signalnbumps256
Annual minimal water levels of Nile rivernile.min
Nyctalus noctula echolocation datanoctula
Monthly Total Vehicle SalesNSA
Daily Number of Chicken-Fried Steaks Soldozona
Compute partial autocorrelationspacfts.wge
Smoothed Periodogram using Parzen Windowparzen.wge
Pennsylvania average monthly temperaturespatemp
Calculate the periodogramperiod.wge
Calculate pi weights for an ARMA modelpi.weights.wge
Plots Discrete Wavelet Transform (DWT)plotts.dwt.wge
Plots MRA plot)plotts.mra.wge
Calculate and plot the periodogram and Parzen window estimates with differing trunctaion pointsplotts.parzen.wge
Plot Data, Sample Autocorrelations, Periodogram, and Parzen Spectral Estimateplotts.sample.wge
Plot of generated data, true autocorrelations and true spectral density for ARMA modelplotts.true.wge
Plot a time series realizationplotts.wge
Data matrix for Problem 10.4 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliottprob10.4
Data for Problem 10.6 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottprob10.6x
Simulated observed data for Problem 10.6 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottprob10.6y
Data for Problem 10.7 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottprob10.7x
Simulated observed data for Problem 10.6 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottprob10.7y
Data for Problem 11.5 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottprob11.5
Data for Problem 12.1c and 12.3c in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottprob12.1c
Data for Problem 12.3a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottprob12.3a
Data for Problem 12.3b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottprob12.3b
Data set for Problem 12.6(C) in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottprob12.6c
Data for Problem 13.2 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottprob13.2
Data for Problem 8.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliottprob8.1a
Data for Problem 8.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliottprob8.1b
Data for Problem 8.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliottprob8.1c
Data for Problem 8.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliottprob8.1d
Data set 1 for Problem 6.1cprob9.6c1
Data set 2 for Problem 6.1cprob9.6c2
Data set 3 for Problem 6.1cprob9.6c3
Data set 4 for Problem 6.1cprob9.6c4
Calculate psi weights for an ARMA modelpsi.weights.wge
Daily DOW rate of Returnrate
Function to Calculate the Rolling Window RMSEroll.win.rmse.nn.wge
Function to Calculate the Rolling Window RMSEroll.win.rmse.wge
Simple Linear Regressionslr.wge
Sunspot Datass08
Sunspot data from 1850 through 2008 for matching with global temperature data (hadley)ss08.1850
Starwort Explosion data shown in Figure 13.13a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliottstarwort.ex
Classic Sunspot Data: 1749-1924sunspot.classic
Annual Sunspot2.0 Numberssunspot2.0
Monthly Sunspot2.0 Numberssunspot2.0.month
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 Elliotttable10.1.noise
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 Elliotttable10.1.signal
MA(2) data for Table 7.1table7.1
Tesla Stock Pricestesla
Transforms TVF data set to a dual data settrans.to.dual.wge
Transforms dual data set back to original time scaletrans.to.original.wge
True ARMA autocorrelationstrue.arma.aut.wge
True ARMA Spectral Densitytrue.arma.spec.wge
True FARMA autocorrelationstrue.farma.aut.wge
True GARMA autocorrelationstrue.garma.aut.wge
Texas Seasonally Adjusted Unnemployment Ratestx.unemp.adj
Texas Unadjusted Unnemployment Ratestx.unemp.unadj
Plot the roots of the characteristic equation on the complex plain.unit.circle.wge
Quarterly US Retail Salesus.retail
US populationuspop
Daily wages in Pounds from 1260 to 1944 for Englandwages
Woodward-Bottone-Gray test for trendwbg.boot.wge
Whale click datawhale
West Texas Intermediate Crude Oil Priceswtcrude
Monthly WTI Crude Oil Priceswtcrude2020
Function to calculate Wigner Ville spectrumwv.wge
Precleaned Yellow Cab datayellowcab.precleaned