Learn R Programming
⚠️There's a newer version (2.1.0) of this package.Take me there.

tswge (version 2.0.0)

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.

Copy Link

Version

Install

install.packages('tswge')

Monthly Downloads

697

Version

2.0.0

License

GPL-2

Maintainer

Bivin Sadler

Last Published

August 9th, 2022

Functions in tswge (2.0.0)

aic.wge

ARMA Model Identification
MedDays

Median days a house stayed on the market
NSA

Monthly Total Vehicle Sales
airline

Classical Airline Passenger Data
Bsales

Toy Data Set of Business Sales Data
aic5.ar.wge

Return top 5 AIC, AICC, or BIC picks for AR model fits
aic.ar.wge

AR Model Identification for AR models
aic5.wge

Return top 5 AIC, AICC, or BIC picks
NAICS

Monthly Retail Sales Data
bumps16

16 point bumps signal
aic.burg.wge

AR Model Identification using Burg Estimates
dfw.yr

DFW Annual Temperatures
dfw.mon

DFW Monthly Temperatures
dowjones2014

Dow Jones daily averages for 2014
fig10.3x1

Variable X1 for the bivariate realization shown in Figure 10.3"
fig1.10d

Simulated data in Figure 1.10d 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
bat

Bat echolocation signal shown in Figure 13.11a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
eco.cd6

6-month rates
fig10.1mort

Data shown in Figure 10.1c 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
bumps256

256 point bumps signal
cement

Cement data shown in Figure 3.30a 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)
doppler2

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

Doppler Data
artrans.wge

Perform Ar transformations
backcast.wge

Calculate backcast residuals
airlog

Natural log of airline data
fig6.7nf

Data in Figure 6.2 without the forecasts
fig10.1bond

Data for Figure 10.1b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
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
fig6.6nf

Data in Figure 6.6 without the forecasts
est.farma.wge

Estimate the parameters of a FARMA model.
est.garma.wge

Estimate the parameters of a GARMA model.
factor.wge

Produce factor table for a kth order AR or MA model
dfw.2011

DFW Monthly Temperatures from January 2011 through December 2020
factor.comp.wge

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

Perform Butterworth Filter
co.wge

Cochrane-Orcutt test for trend
chirp

Chirp data shown in Figure 12.2a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
flu

Influenza data shown in Figure 10.8 (dotted line)
fig8.8a

Data for Figure 8.8a in Applied time series Analysis with R, second edition by Woodward, Gray, and Elliott
eco.corp.bond

Corporate bond rates
fore.sigplusnoise.wge

Forecasting signal plus noise models
eco.mort30

30 year mortgage rates
dow1985

Daily DOW Closing Prices 1985 through 2020
dow1000

Dow Jones daily rate of return data for 1000 days
fig10.1cd

Data shown in Figure 10.1a 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
ma.pred.wge

Predictive or rolling moving average
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
freeze

Minimum temperature data
nile.min

Annual minimal water levels of Nile river
global.temp

Global Temperature Data: 1850-2009
gen.sigplusnoise.wge

Generate data from a signal-plus-noise model
ma.smooth.wge

Centered Moving Average Smoother
noctula

Nyctalus noctula echolocation data
fig1.10b

Simulated data shown in Figure 1.10b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
fig1.10a

Simulated data shown in Figure 1.10a 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
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
cardiac

Weekly Cardiac Mortality Data
fig6.11a

Cyclical Data
fig6.1nf

Data in Figure 6.1 without the forecasts
plotts.parzen.wge

Calculate and plot the periodogram and Parzen window estimates with differing trunctaion points
gen.garch.wge

Generate a realization from a GARCH(p0,q0) model
fore.glambda.wge

Forecast using a G(lambda) model
expsmooth.wge

Exponential Smoothing
est.glambda.wge

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

Forecast using a GARMA model
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
prob12.3a

Data for Problem 12.3a 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
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
ss08

Sunspot Data
plotts.sample.wge

Plot Data, Sample Autocorrelations, Periodogram, and Parzen Spectral Estimate
starwort.ex

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

Function to generate a GARMA realization
fig13.18a

Simulated data shown in Figure 3.18a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
fig1.22a

White noise data
ljung.wge

Ljung-Box Test
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
hadley

Global temperature data
linearchirp

Linear chirp data.
global2020

Global Temperature Data: 1880-2009
mass.mountain

Massachusettts Mountain Earthquake Data
prob10.6y

Simulated observed data for Problem 10.6 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
fig6.8nf

Simulated seasonal data with s=12
dow.rate

DOW Daily Rate of Return Data
dow.annual

DOW Annual Closing Averages
fore.arima.wge

Function for forecasting from known model which may have (1-B)^d and/or seasonal factors
sunspot2.0.month

Monthly Sunspot2.0 Numbers
fig8.4a

Data for Figure 8.4a in Applied time series Analysis with R, second 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
fig8.6a

Data for Figure 8.6a in Applied time series Analysis with R, second edition by Woodward, Gray, and Elliott
gen.arma.wge

Function to generate an ARMA realization
est.ar.wge

Estimate parameters of an AR(p) model
gen.aruma.wge

Function to generate an ARUMA (or ARMA or ARIMA) realization
fig1.5

Simulated data shown in Figure 1.5 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
fig4.8a

Gaussian White Noise
fore.arma.wge

Forecast from known model
est.arma.wge

Function to calculate ML estimates of parameters of stationary ARMA models
gegenb.wge

Calculates Gegenbauer polynomials
freight

Freight data
fig1.16a

Simulated data for Figure 1.16a 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
gen.geg.wge

Function to generate a Gegenbauer realization
is.sample.wge

Sample instantaneous spectrum based on periodogram
lavon

Lavon lake water levels
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
unit.circle.wge

Plot the roots of the characteristic equation on the complex plain.
gen.glambda.wge

Function to generate a g(lambda) realization
kalman.miss.wge

Kalman filter for simple signal plus noise model with missing data
fig1.21a

Simulated shown in Figure 1.21a of Woodward, Gray, and Elliott text
prob8.1b

Data for Problem 8.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott
table7.1

MA(2) data for Table 7.1
llynx

Log (base 10) of lynx data
prob10.7x

Data for Problem 10.7 in Applied Time Series Analysis with R, second 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
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
wbg.boot.wge

Woodward-Bottone-Gray test for trend
ma2.table7.1

Simulated MA(2) data
lynx

Lynx data
plotts.mra.wge

Plots MRA plot)
tx.unemp.adj

Texas Seasonally Adjusted Unnemployment Rates
tx.unemp.unadj

Texas Unadjusted Unnemployment Rates
prob8.1a

Data for Problem 8.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott
sunspot2.0

Annual Sunspot2.0 Numbers
prob13.2

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

Classic Sunspot Data: 1749-1924
plotts.dwt.wge

Plots Discrete Wavelet Transform (DWT)
fig3.16a

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

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

Smoothed Periodogram using Parzen Window
fig3.18a

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

Instantaneous spectrum
fig6.2nf

Data in Figure 6.2 without the forecasts
patemp

Pennsylvania average monthly temperatures
fig6.5nf

Data in Figure 6.5 without the forecasts
gen.arch.wge

Generate a realization from an ARCH(q0) model
fore.aruma.wge

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

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

Forecast using a FARMA model
prob10.4

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

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

Function to generate an ARIMA (or ARMA) realization
prob10.6x

Data for Problem 10.6 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
prob9.6c4

Data set 4 for Problem 6.1c
whale

Whale click data
ozona

Daily Number of Chicken-Fried Steaks Sold
plotts.true.wge

Plot of generated data, true autocorrelations and true spectral density for ARMA model
prob10.7y

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

Calculate psi weights for an ARMA model
lavon15

Lavon Lake Levels to September 30, 2015
pacfts.wge

Compute partial autocorrelations
plotts.wge

Plot a time series realization
prob8.1d

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

Daily DOW rate of Return
prob11.5

Data for Problem 11.5 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
prob9.6c1

Data set 1 for Problem 6.1c
ample.spec.wge

Smoothed Periodogram using Parzen Window
roll.win.rmse.wge

Function to Calculate the Rolling Window RMSE
kingkong

King Kong Eats Grass
true.arma.spec.wge

True ARMA Spectral Density
true.farma.aut.wge

True FARMA autocorrelations
mult.wge

Multiply Factors
nbumps256

256 noisy bumps signal
period.wge

Calculate the periodogram
tswge-package

Time Series package for Woodward, Gray, and Elliott text
roll.win.rmse.nn.wge

Function to Calculate the Rolling Window RMSE
wtcrude

West Texas Intermediate Crude Oil Prices
tesla

Tesla Stock Prices
wtcrude2020

Monthly WTI Crude Oil Prices
true.garma.aut.wge

True GARMA autocorrelations
wages

Daily wages in Pounds from 1260 to 1944 for England
uspop

US population
trans.to.dual.wge

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

Data for Problem 12.3b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
pi.weights.wge

Calculate pi weights for an ARMA model
prob12.6c

Data set for Problem 12.6(C) in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
slr.wge

Simple Linear Regression
ss08.1850

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

True ARMA autocorrelations
trans.to.original.wge

Transforms dual data set back to original time scale
yellowcab.precleaned

Precleaned Yellow Cab data
wv.wge

Function to calculate Wigner Ville spectrum