Time Series package for Woodward, Gray, and Elliott text

Description

These functions and data sets accompany the book "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Author(s)

Wayne Woodward <waynew@smu.edu>

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(wages)
plotts.wge(wages) 

Toy Data Set of Business Sales Data

Description

100 weeks of sales data with sales, TV advertising budget, Online advertising budget and the abount of a discount if any.

Usage

data("Bsales")

References

The Time Series Toolkit

Examples

data(Bsales)

Median days a house stayed on the market

Description

Median days a house stayed on the market between July 2016 and April 2020

Usage

data("MedDays")

Format

ts object consisting of monthly data from July 2016 through April 2020

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(MedDays)

Monthly Retail Sales Data

Description

Monthly sales for the North American Industry Classification System (NAICS) code 44X72: Retail Trade and Food Services: 1992-2019

Usage

data("NAICS")

Format

ts object consisting of monthly data from January 1992- December 2019

Source

https://www.weather.gov/fwd/dmotemp

References

"Kaggle" and "US Census Bureau" websites

Examples

data(NAICS)

Monthly Total Vehicle Sales

Description

Monthly Total Vehicle Sales (TOTALNSA) in the United States from January 1976 - December 2019

Usage

data("NSA")

Format

ts object consisting of monthly data from January 1976- December 2019

Source

https://www.weather.gov/fwd/dmotemp

References

"Kaggle" and "US Census Bureau" websites

Examples

data(NSA)

AR Model Identification for AR models

Description

AR model identification using either AIC, AICC, or BIC and MLE, Burg or YW

Usage

aic.ar.wge(x, p = 1:5, type = "aic",method='mle')

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig3.18a)
          aic.ar.wge(fig3.18a,p=1:5,type='aicc',method='burg')

AR Model Identification using Burg Estimates

Description

AR model identification using either AIC, AICC, or BIC

Usage

aic.burg.wge(x, p = 1:5, type = "aic")

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig3.18a)
          aic.burg.wge(fig3.18a,p=1:5,type='aicc')

ARMA Model Identification

Description

ARMA model identification using either AIC, AICC, or BIC

Usage

aic.wge(x, p = 0:5, q = 0:2, type = "aic")

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig3.18a)
          aic.wge(fig3.18a,p=0:5,q=0:1,type='aicc')

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

Description

You may select either AIC, AICC, or BIC to use model identification. You can also used ML, Burg, or Yule-Walker estimates. Given a range of values for p and q, the program returns the top 5 candidate models.

Usage

aic5.ar.wge(x, p = 0:5, type = "aic",method='mle')

Arguments

Value

A list of p, selected criterion for the top 5 models. The identification type and estimation method are printed on the output.

Note

If some model order combinations give explosively nonstationary models, then the program may stop prematurely. You may need to adjust the range of p and q to avoid these models.

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig3.18a)
          aic5.wge(fig3.18a,p=0:5,q=0:2)

Return top 5 AIC, AICC, or BIC picks

Description

You may select either AIC, AICC, or BIC to use model identification. Given a range of values for p and q, the program returns the top 5 candidate models.

Usage

aic5.wge(x, p = 0:5, q = 0:2, type = "aic")

Arguments

Value

A list of p,q, and selected criterion for the top 5 models

Note

If some model order combinations give explosively nonstationary models, then the program may stop prematurely. You may need to adjust the range of p and q to avoid these models.

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig3.18a)
          aic5.wge(fig3.18a,p=0:5,q=0:2)

Classical Airline Passenger Data

Description

Monthly international airline passengers (in 1000s) from January 1949-December 1960. Series G in Box, Jenkings, and Reinsel text

Usage

data("airline")

Format

The format is: num [1:144] 112 118 132 129 121 135 148 148 136 119 ...

Source

"Time Series Analysis: Forecasting and Control" by Box, Jenkins, and Reinsel

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(airline)

Natural log of airline data

Description

Natural log of monthly international airline passengers (in 1000s) from January 1949-December 1960. Series G in Box, Jenkings, and Reinsel text

Usage

data("airlog")

Format

The format is: num [1:144] 4.72 4.77 4.88 4.86 4.8 ...

Source

"Time Series Analysis: Forecasting and Control" by Box, Jenkins, and Reinsel

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(airlog)

Smoothed Periodogram using Parzen Window

Description

This function calculates and optionally plots the smoothed periodogram using the Parzen window. The truncation point may be chosen by the user

Usage

sample.spec.wge(x, dbcalc = "TRUE", plot = "TRUE")

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

sample.spec.wge(rnorm(100))

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

Description

Annual non-perforated appendicitis rates for years 1970-2005

Usage

data("appy")

Format

The format is: num [1:36] 14.8 13.7 14.3 14.2 13 ...

Source

Alder, et al. (2010)Archives of Surgery 145, 63-71

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(appy)

Perform Ar transformations

Description

Given a time series in the vector x, and AR coefs phi1 and phi2, for example, artrans.wge computes y(t)=x(t)-phi1X(t-1)-phi2x(t-2), for t=3, ..., n

Usage

artrans.wge(x,phi.tr, lag.max=25, plottr = "TRUE")

Arguments

Value

Transformed data

Note

For a difference, use phi.tr=1

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott"

Examples

data(wtcrude)
difdata=artrans.wge(wtcrude,phi.tr=1,lag.max=30,plottr=TRUE)

Calculate backcast residuals

Description

This function takes either a fitted (or true) model for the realization x and calculates the residuals using the backcasting procedure

Usage

backcast.wge(x, phi = 0, theta = 0, n.back = 50)

Arguments

Value

The n backcast residuals are returned

Author(s)

Wayne Woodward

References

Chapter 7 of Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig6.2nf)
          backcast.wge(fig6.2nf,phi=c(1.2,-.6),theta=.5,n.back=50)

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

Description

Bat echolocation signal of a big brown bat

Usage

data("bat")

Format

The format is: num [1:381] -0.0049 -0.0083 0.0127 0.0068 -0.0259 0.0059 0.0386 -0.0405 -0.0269 0.0474 ...

Source

Al Feng, Beckman Center of the University of Illinois

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(bat)

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

Description

This dataset contains the daily price of bitcoin from May 1, 2021 to April 30, 2021. The data was gathered from Yahoo Finance on April 30, 2020 and included missing values on October 9, 12 and 13 of 2020. Yahoo Finance has since filled in the correct values which can be compared with the imputed values described in the book.

Usage

data("bitcoin")

Format

The format is: num [1:461] 7200.174 6985.470 7344.884 ...

Source

Yahoo Finance

References

"Practical Time Series for Data Scientiests by Woodward, Sadler and Robertson"

Examples

data(bitcoin)

16 point bumps signal

Description

Bumps signal from Donoho and Johnstone(1994) Biometrika 81,425-455

Usage

data("bumps16")

Format

The format is: num [1:16] 0.1 0.4 5.5 0.2 1.4 0.5 0.3 0.7 0.1 2.5 ...

Source

Donoho and Johnstone(1994) Biometrika 81,425-455

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(bumps16)

256 point bumps signal

Description

Bumps signal from Donoho and Johnstone(1994) Biometrika 81,425-455

Usage

data("bumps256")

Format

The format is: num [1:256] 0.00016 0.00017 0.000182 0.000195 0.000211 ...

Source

Donoho and Johnstone(1994) Biometrika 81,425-455

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(bumps256)

Perform Butterworth Filter

Description

The user can specify the order of the filter, and whether it is low pass ("low"), high pass ("high"), band stop ("stop"), or band pass ("pass") filter. Requires the CRAN package 'signal'.

Usage

butterworth.wge(x, order, type, cutoff,plot=TRUE)

Arguments

Value

The filtered data

Note

Requires CRAN package 'signal'

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(wages)
    butterworth.wge(wages,order=4,type="low",cutoff=.05)

Weekly Cardiac Mortality Data

Description

Weekly cardiac mortality, temperatures, and pollution measures for the years 1970-1978

Usage

data("cardiac")

Format

ts object consisting of weekly data

Source

Shumway and Stoffer, 1999)

References

"Time Series for Data Sience:Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(cardiac)

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

Description

Quarterly usage of metric tons (in thousands) of Portland cement used from the first quarter of 1973 through the fourth quarter of 1993 in Australia

Usage

data("cement")

Format

The format is: num [1:84] 1148 1305 1342 1452 1184 ...

Source

Australian Bureau of Statistics

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(cement)

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

Description

256 point linear chirp data

Usage

data("chirp")

Format

The format is: List of 2 $ x : num [1:256] 1 1 0.98 0.95 0.91 0.86 0.8 0.72 0.63 0.53 ... $ spec: num [1:256] 0.511 0.568 0.733 0.991 1.32 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(chirp)

Cochrane-Orcutt test for trend

Description

Performs the Cochrane-Orcutt to test for a linear trend in a time series realization.)

Usage

co.wge(x,maxp=5)

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(global.temp)
          co.wge(global.temp,maxp=5)

DFW Monthly Temperatures from January 2011 through December 2020

Description

Monthly average temperatures at Dallas Ft. Worth (in Fahrenheit) from January 2011 through December 2020

Usage

data("dfw.2011")

Format

ts object consisting of monthly data from January 1900 trough December 2020

Source

https://www.weather.gov/fwd/dmotemp

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(dfw.2011)

DFW Monthly Temperatures

Description

Monthly average temperatures at Dallas Ft. Worth (in Fahrenheit) from January 1900 through December 2020

Usage

data("dfw.mon")

Format

ts object consisting of monthly data from January 1900 through December 2020

Source

https://www.weather.gov/fwd/dmotemp

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(dfw.mon)

DFW Annual Temperatures

Description

Annual average temperatures at Dallas Ft. Worth (in Fahrenheit) from January 1900 through December 2020

Usage

data("dfw.yr")

Format

ts object consisting of annual data from 1900 through 2020

Source

https://www.weather.gov/fwd/dmotemp

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(dfw.yr)

Doppler Data

Description

Generated Doppler data

Usage

data("doppler")

Format

The format is: num [1:2000] -0.00644 -0.01739 -0.02961 -0.04091 -0.04952 ...

Source

Simulated

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(doppler)

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

Description

Doppler signal with two time-varying frequencies

Usage

data("doppler2")

Format

The format is: num [1:200] -0.372 1.246 -1.163 0.261 -0.698 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(doppler2)

DOW Annual Closing Averages

Description

DOW Annual closing averages from 1915 through 2020

Usage

data("dow.annual")

Format

ts object consisting of DOW Annual closing averages from 19155 through 2020

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(dow.annual)

DOW Daily Rate of Return Data

Description

DOW daily rate of return data from October 1, 1928 to December 31, 2010

Usage

data("dow.rate")

Format

The format is: num [1:20656] 240 238 238 240 240 ...

Source

Public access

References

"Applied Statistics and Data Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(dow.rate)

Dow Jones daily rate of return data for 1000 days

Description

Dow Jones daily rate of return for the 1000 trading days before December 31, 2010.

Usage

data("dow1000")

Format

The format is: num [1:1001] 240 238 238 240 240 ...

Source

Internet and shown in Figure 4.9, "Applied Time Series Analysis with R, 2nd edition", by Woodward, Gray and Elliott

Examples

data(dow1000)

Daily DOW Closing Prices 1985 through 2020

Description

Daily DOW Closing Prices 1985 through 2020

Usage

data("dow1985")

Format

ts object consisting of daily dow closing prices from 1985 through 2020

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(dow1985)

Dow Jones daily averages for 2014

Description

Daily Dow Jones averages for 2014

Usage

data("dowjones2014")

Format

The format is: num [1:252] 16441 16470 16425 16531 16463 ...

Source

Economic Data: Federal Reserve Bank of St. Louis. Website: https://research.stlouisfed.org/fred2/series/DJIA/downloaddata

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(dowjones2014)

6-month rates

Description

6-month rates 1/1/1991 through 4/1/2010

Usage

data("eco.cd6")

Format

The format is: num [1:469] 7.25 7.53 7.64 7.64 7.59 7.44 7.39 7.26 7.25 7.19 ...

Source

Internet

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(eco.cd6)

Corporate bond rates

Description

Corporate bond rates 1/1/1991 through 4/1/2010

Usage

data("eco.corp.bond")

Format

The format is: num [1:469] 4.61 5.22 5.69 6.04 6.06 5.91 5.43 5.04 4.89 4.26 ...

Source

Internet

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(eco.corp.bond)

30 year mortgage rates

Description

30-year mortgage rates 1/1/1991 through 4/1/2010

Usage

data("eco.mort30")

Format

The format is: num [1:469] 7.31 7.43 7.53 7.6 7.7 7.69 7.63 7.55 7.48 7.44 ...

Source

Internet

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(eco.mort30)

Estimate parameters of an AR(p) model

Description

Estimate parameters of an AR(p) with p assumed known. Outputs residuals (backcast0 and white noise variance estimate.)

Usage

est.ar.wge(x, p = 2, factor = TRUE, method = "mle")

Arguments

Details

The 'type' arument is added for backwards compatabililty and if specified will replace the value specified in the 'method' argument.

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig6.1nf)
          est.ar.wge(fig6.1nf,p=1)

Function to calculate ML estimates of parameters of stationary ARMA models

Description

This function calculates ML estimates, computes residuals (using backcasting), estimates white noise variance for a stationary ARMA model

Usage

est.arma.wge(x, p = 0, q = 0, factor = TRUE)

Arguments

Details

This function uses arima from base SAS and is written similarly to itsmr function arma

Value

Note

Requires CRAN package 'itsmr'. The program is based on arima from base R and arma from 'itsmr'

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig6.2nf)
          est.arma.wge(fig6.2nf,p=2,q=1)

Estimate the parameters of a FARMA model.

Description

This function uses the grid search algorithm discussed in Section 11.5 of Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Usage

est.farma.wge(x, low.d, high.d, inc.d, p.max, nback = 500)

Arguments

Details

We assume q=0 and do not allow moving average terms in the model.

Value

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott. See also Hosking (1984)

Examples

est.farma.wge(Nile,low.d=.1,high.d=.5,inc.d=.01,p.max=3)

Estimate the parameters of a GARMA model.

Description

This function uses the grid search algorithm discussed in Section 11.5 of Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Usage

est.garma.wge(x,low.u,low.lambda,high.u,high.lambda,inc.u,inc.lambda,p.max,nback=500)

Arguments

Details

We assume q=0 and do not allow moving average terms in the model.

Value

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott. See also Hosking (1984), Gray, Zhang, and Woodward(1989), and Woodward, Cheng, and Gray(1998)

Examples

data(llynx)
est.garma.wge(llynx,low.u=.4,high.u=.9,low.lambda=.2,high.lambda=.4,inc.u=.01,inc.lambda=.1,p.max=1)

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

Description

This function uses the technique discussed in Section 13.3.3 of Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott to find the g(lambda) time transformation that most nearly transforms the data to a stationary dual.

Usage

est.glambda.wge(data, lambda.range = c(0, 1), offset.range = c(0, 100))

Arguments

Value

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott and Jiang, Gray, and Woodward(2006)

Examples

data(ss08)
                   est.glambda.wge(ss08,lambda.range=c(-1,1),offset.range=c(0,100))

Exponential Smoothing

Description

Performs exponential smoothing on the data in vector x

Usage

expsmooth.wge(x,alpha=NULL,n.ahead=0,plot=TRUE)

Arguments

Value

Author(s)

Wayne Woodward

References

"Time Series for Data Science" by Woodward, Sadler, and Robertson

Examples

data(wtcrude2020)
expsmooth.wge(wtcrude2020)

Create a factor table and AR components for an AR realization

Description

This program finds the ML estimates of a specified order, then prints a factor table for the estimated model and prints and plots the additive components

Usage

factor.comp.wge(x, aic = FALSE, p, ncomp)

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Elliott, and Gray

Examples

data(ss08)
    factor.comp.wge(ss08,p=9,ncomp=4)

Produce factor table for a kth order AR or MA model

Description

This program produces a factor table that reduces a kth order factor into its first and irreducible second order factors as described in Section 3.2.11 of "Applied Time Series Analysis" by Woodward, Gray, and Elliott

Usage

factor.wge(phi=0, theta=0)

Arguments

Value

The only output is the factor table, written by default to the console

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis, 2nd edition" by Woodward, Gray, and Elliott

Examples

factor.wge(phi=c(-.3,.44,.29,-.378,-.648))

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

Description

This is the sum of the three signals in fig1.10b, fig1.10c, and fig1.10d

Usage

data("fig1.10a")

Format

The format is: num [1:1000] 0.0217 -0.1528 -0.3141 -0.4613 -0.5934 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig1.10a)

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

Description

Low frequency component of Figure 1.10a

Usage

data("fig1.10b")

Format

The format is: num [1:1000] 1 1 0.999 0.998 0.997 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

 data(fig1.10b)

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

Description

Middle frequencies component in Figure 1.10a

Usage

data("fig1.10c")

Format

The format is: num [1:1000] 0.73 0.646 0.56 0.471 0.381 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig1.10c)

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

Description

High frequency component of Figure 1.10a

Usage

data("fig1.10d")

Format

The format is: num [1:1000] -1.71 -1.8 -1.87 -1.93 -1.97 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig1.10d)

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

Description

Data containing two dominant frequencies

Usage

data("fig1.16a")

Format

The format is: num [1:250] -0.89 -3.209 0.929 -0.763 -1.972 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig1.16a)

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

Description

Simulated shown in Figure 1.21a of Woodward, Gray, and Elliott text. It illustrates the fact that frequency information is displayed better in the spectrum than the autocorrelations.

Usage

data("fig1.21a")

Format

The format is: num [1:250] -0.89 -3.209 0.929 -0.763 -1.972 ...

Source

Simulated by the authors of the Woodward, Gray, and Elliott text

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig1.21a)

White noise data

Description

Realization of length n=250 of white noise data, Figure 1.22a in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

data("fig1.22a")

Format

The format is: num [1:250] 0.302 -0.691 -0.477 0.814 -0.267 ...

Source

Simulated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig1.22a)

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

Description

Simulated data from an ergodic AR(1) process

Usage

data("fig1.5")

Format

The format is: num [1:100] 0.739 -0.39 0.15 -0.627 0.262 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig1.5)

Simulated data shown in Figure 10.11 (solid line) in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Description

Simulated unobservable AR(1) data in Example 10.11

Usage

data("fig10.11x")

Format

The format is: num [1:75] -0.2497 -0.0812 -0.6463 -1.7653 -2.719 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig10.11x)

Simulated data shown in Figure 10.11 (dashed line) in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Description

Simulated observed AR(1) plus noise data in Example 10.11

Usage

data("fig10.11y")

Format

The format is: num [1:75] -0.74 0.045 -0.775 -2.944 -2.278 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig10.11y)

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

Description

Moody's seasoned Aaa corporate bond rate, January 1, 1991-April1, 2010

Usage

data("fig10.1bond")

Format

The format is: num [1:232] 7.17 6.51 6.5 6.16 6.03 6.26 6.25 5.79 5.6 5.32 ...

Source

Internet

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig10.1bond)

Data shown in Figure 10.1a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Description

6 month CD rate for January 1, 1991 - April 1, 2010

Usage

data("fig10.1cd")

Format

The format is: num [1:232] 9.04 8.83 8.93 8.86 8.86 9.01 9 8.75 8.61 8.55 ...

Source

Internet

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig10.1cd)

Data shown in Figure 10.1c in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Description

30 year conventional mortgage rates: January 1, 1991-April1, 2010

Usage

data("fig10.1mort")

Format

The format is: num [1:232] 9.64 9.37 9.5 9.49 9.47 9.62 9.58 9.24 9.01 8.86 ...

Source

Internet

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig10.1mort)

Variable X1 for the bivariate realization shown in Figure 10.3"

Description

Variable X1 for the bivariate Var1) realization in Figure 10.3 of "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

data("fig10.3x1")

Format

The format is: num [1:75] -0.0757 -0.2728 -0.8089 -2.4747 -5.9256 ...

Source

Simulated Var(1) data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig10.3x1)

Variable X2 for the bivariate realization shown in Figure 10.3"

Description

Variable X2 for the bivariate Var1) realization in Figure 10.3 of "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

data("fig10.3x2")

Format

The format is: num [1:75] 0.646 -1.313 -0.191 -2.61 -4.925 ...

Source

Simulated Var(1) data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig10.3x2)

Data shown in Figure 11.12a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Description

Simulated GATMA(1,0) data

Usage

data("fig11.12")

Format

The format is: num [1:500] 2.18 -1.17 -3.13 -1.32 1.69 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig11.12)

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

Description

Simulated FARMA(2,0) data

Usage

data("fig11.4a")

Format

The format is: num [1:100] 1.361 -0.369 0.881 2.362 0.236 ...

Source

simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig11.4a)

Simulated data with two frequencies shown in Figure 12.1a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Description

Simulated two-frequency data in which the two frequencies are separated in time

Usage

data("fig12.1a")

Format

The format is: num [1:200] -1.22 -6.06 -9.66 -10.14 -8.58 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig12.1a)

Simulated data with two frequencies shown in Figure 12.1b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Description

Simulated two-frequency AR(4) data

Usage

data("fig12.1b")

Format

The format is: num [1:256] 10.081 10.835 0.532 -5.495 1.294 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig12.1b)

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

Description

Simulated AR(4) data

Usage

data("fig13.18a")

Format

The format is: num [1:400] 1.251 1.0019 -0.0317 -1.0167 -1.4222 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig13.18a)

TVF data shown in Figure 13.2c in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Description

Realization from an Euler(2) model

Usage

data("fig13.2c")

Format

The format is: num [1:200] -13.14 -11.03 22.06 -8.92 -16.67 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig13.2c)

AR(2) Realization (1-.95)^2X(t)=a(t)

Description

AR(2) Realization (1-.95)^2X(t)=a(t) plotted in Figure 3.10d in "Applied Time series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

data("fig3.10d")

Format

The format is: num [1:100] 15.3 16.3 18.6 21.2 22.8 ...

Details

This realization is also used in Chapter 7 of text above for testing estimation techniques

Source

Simulated realization

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig3.10d)

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

Description

Realization from the AR(3) model in Figure 3.16a

Usage

data("fig3.16a")

Format

The format is: num [1:200] -0.0686 0.4304 0.4786 0.9899 3.4047 ...

Source

Simulated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig3.16a)

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

Description

Realization from the AR(3) model in Figure 3.18a

Usage

data("fig3.18a")

Format

The format is: num [1:200] -0.573 -0.837 -1.16 1.078 -0.561 ...

Source

Simulated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig3.18a)

ARMA(2,1) realization

Description

ARMA(2,1) realization of length n=200 phi(1)=1.6,phi(2)=-.9,theta(1)=.8 (using Box-Jenkins-Reinsel notation)

Usage

data("fig3.24a")

Format

The format is: num [1:200] 0.685 -1.234 -0.714 0.796 -0.96 ...

Source

Simulated data

References

Fig3.24a in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig3.24a)

Simulated data shown in Figure 3.29a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Description

Simulated data from stationary seasonal model

Usage

data("fig3.29a")

Format

The format is: num [1:20] -7.23 -6.99 -6.9 -6.26 -3.79 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig3.29a)

Gaussian White Noise

Description

Gaussian White Noise, n=1000 shown in Figure 4.8a in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

data("fig4.8a")

Format

The format is: num [1:1000] -0.585 0.177 0.284 -0.271 0.126 ...

Source

Simulated data

References

Plotted in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig4.8a)

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

Description

Realization of length 200 from the AR(3) model (1-.995B)(1-1.2B+.8B^2)X(t)=a(t)

Usage

data("fig5.3c")

Format

The format is: num [1:200] -0.503 -0.811 -0.188 1.34 2.982 ...

Source

Simulated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig5.3c)

Cyclical Data

Description

First 50 points of data in Figure 6.11a, Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Usage

data("fig6.11a")

Format

The format is: num [1:50] -0.682 0.15 2.262 3.079 4.122 ...

Source

Simulated

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig6.11a)

Data in Figure 6.1 without the forecasts

Description

Realization from the AR(1) model (1-.8B)(X(t)-25)=a(t) in Figure 6.2 and also shown in Table 6.1 of "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

data("fig6.1nf")

Format

The format is: num [1:80] 25.1 27.1 27.3 25.7 23.9 ...

Source

Generated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig6.1nf)

Data in Figure 6.2 without the forecasts

Description

Realization from the ARMA(2,1) model (1-1.2B+.6B^2)(X(t)-50)=(1-.5B)a(t) in Figure 6.2 and also shown in Table 6.1 of "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

data("fig6.2nf")

Format

The format is: num [1:25] 49.5 51.1 50 49.7 50.4 ...

Source

Generated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig6.2nf)

Data in Figure 6.5 without the forecasts

Description

Realization from the ARIMA(0,1,0) model for realization in Figure 6.5 of "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

data("fig6.5nf")

Format

The format is: num [1:50] 105 104 103 102 102 ...

Source

Generated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig6.5nf)

Data in Figure 6.6 without the forecasts

Description

Realization from the ARIMA(1,1,0) model (1-.8B)(1-B)X(t)=a(t) for realization in Figure 6.6 of "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

data("fig6.6nf")

Format

The format is: num [1:50] 139 138 138 140 141 ...

Source

Generated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig6.6nf)

Data in Figure 6.2 without the forecasts

Description

Realization from the ARIMA(0,2,0) model for realization in Figure 6.7 of "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

data("fig6.7nf")

Format

The format is: num [1:50] -582 -579 -578 -578 -579 ...

Source

Generated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig6.7nf)

Simulated seasonal data with s=12

Description

Simulated seasonal data designed for showing seasonal forecasts

Usage

data("fig6.8nf")

Format

The format is: num [1:48] 5.8 13.66 9.83 7.33 6.96 ...

Source

Simulated Data

References

"Applied Time series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig6.8nf)

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

Description

Realization of length n=200 from the model (1-B)(1-1.79B+1.75B^2-1.61B^3+.765B^4)X(t)=a(t)

Usage

data("fig8.11a")

Format

The format is: num [1:200] 83.2 80.9 78.9 80.4 85.4 ...

Source

Simulated data

References

Applied time series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig8.11a)

Data for Figure 8.4a in Applied time series Analysis with R, second edition by Woodward, Gray, and Elliott

Description

Realization of length n=200 from the model (1-.8B)(1-1.6B+.995B^2)X(t)=a(t)

Usage

data("fig8.4a")

Format

The format is: num [1:200] 13.45 -5.52 -19 -21.26 -13.63 ...

Source

simulated data

References

Applied time series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig8.4a)

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

Description

The realization of length n=200 is from the model (1-B)^2(1-1.2B+.6B^2)X(t)=a(t)

Usage

data("fig8.6a")

Format

The format is: num [1:200] 354 368 383 399 417 ...

Source

Simulated data

References

Applied time series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig8.6a)

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

Description

Realization of length n=200 from the model (1-B^12)(1-1.25B+.9B^2)(X(t)-50)=a(t)

Usage

data("fig8.8a")

Format

The format is: num [1:200] 48.9 42.9 49.3 57.3 55.5 ...

Source

Simulated data

References

Applied time series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig8.8a)

Influenza data shown in Figure 10.8 (dotted line)

Description

Annual influenza rate for years 1970-2005

Usage

data("flu")

Format

The format is: num [1:36] 9.75 5.82 10.99 10.41 8.42 ...

Source

Alder, et al. (2010)Archives of Surgery 145, 63-71

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(flu)

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

Description

This function calculates forecasts from a known model that may have stationary ARMA components as well as (1-B)^dand/or seasonal factors

Usage

fore.arima.wge(x,phi=0,theta=0,d=0,s=0,n.ahead=5,lastn=FALSE,plot=TRUE,alpha=.05,limits)

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(airline)
          x=log(airline)
          phi12=c(-.36,-.05,-.14,-.11,.04,.09,-.02,.02,.17,.03,-.1,-.38)
          s=12
          d=1
          fore.arima.wge(x,phi=phi12,d=1,s=12,n.ahead=12,limits=FALSE)

Forecast from known model

Description

Forecasts and associated plots for an ARMA model

Usage

fore.arma.wge(x,phi=0,theta=0,n.ahead=5,lastn=FALSE,plot=TRUE,alpha=.05,limits=TRUE)

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig6.1nf)
fore.arma.wge(fig6.1nf,phi=.8,n.ahead=20)

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

Description

This function calculates forecasts from a known model that may have stationary ARMA components as well as (1-B)^d, seasonal, and/or other nonstationary factors

Usage

fore.aruma.wge(x,phi=0,theta=0,d=0,s=0,lambda=0,n.ahead=5,
lastn=FALSE,plot=TRUE,alpha=.05,limits=TRUE)

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(airline)
          x=log(airline)
          phi12=c(-.36,-.05,-.14,-.11,.04,.09,-.02,.02,.17,.03,-.1,-.38)
          s=12
          d=1
          fore.aruma.wge(x,phi=phi12,d=1,s=12,n.ahead=12,limits=FALSE)

Forecast using a FARMA model

Description

Find forecasts using a specified FARMA model

Usage

fore.farma.wge(x, d,  phi, theta = 0, n.ahead = 10, lastn = TRUE, plot = TRUE)

Arguments

Details

Forecasts for an AR model fit to the data are also calculated and optionally plotted

Value

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

fore.farma.wge(Nile, d=.37, phi=0, theta = 0, n.ahead = 30, lastn = TRUE, plot = TRUE)

Forecast using a GARMA model

Description

Find forecasts using a specified GARMA model

Usage

fore.garma.wge(x,u,lambda,phi,theta=0,n.ahead=10,lastn=TRUE,plot=TRUE)

Arguments

Details

Forecasts for an AR model fit to the data are also calculated and optionally plotted

Value

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(llynx)
fore.garma.wge(llynx,u=.796,lambda=.4,phi=.51,theta=0,n.ahead=30,lastn=TRUE,plot=TRUE)

Forecast using a G(lambda) model

Description

Find forecasts using a specified G(lambda) model

Usage

fore.glambda.wge(data.orig,lambda=0,offset=60,phi=0,h=0,n.ahead=10,lastn=TRUE,plot=TRUE)

Arguments

Details

Forecasts for an AR model fit to the data in the original time scale are also calculated and optionally plotted

Value

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig13.2c)
fore.glambda.wge(fig13.2c,lambda=-.4,offset=63,phi=c(0.93,-0.32,-0.15,-0.15,-0.17),n.ahead=30)

Forecasting signal plus noise models

Description

Forecast models of the form line plus AR noise or cosine plus AR noise with known frequency

Usage

fore.sigplusnoise.wge(x,linear=TRUE,method="mle",freq=0,max.p=5,
n.ahead=10,lastn=FALSE,plot=TRUE,alpha=.05,limits=TRUE)

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(llynx)
llynx.for=fore.sigplusnoise.wge(llynx,linear=FALSE,freq=.1,max.p=5,n.ahead=20)

Minimum temperature data

Description

Each data value represents the minimum temperature over 10-day period at a location in South America

Usage

data("freeze")

Format

The format is: num [1:500] 8.2 12.3 9.2 8.4 10 8.8 6.8 4.8 5.2 1.7 ...

Source

Unknown

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(freeze)

Freight data

Description

9 years of monthly freight shipment data

Usage

data("freight")

Format

The format is: num [1:120] 1299 1148 1345 1363 1374 ...

Source

Unknown

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(freight)

Calculates Gegenbauer polynomials

Description

Calculates Gegenbauer polynomials of order n with parameters u and lambda - see (11.9) in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Usage

gegenb.wge(u, d, n)

Arguments

Details

This function is called by gen.garma.wge

Value

The coefficients of the nth order Gegenbauer polynomial

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

gegenb.wge(u=.8,d=.3,n=6)

Generate a realization from an ARCH(q0) model

Description

Generates a realization of length n from the GARCH(q0) model (4.23) in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

gen.arch.wge(n, alpha0, alpha, plot = TRUE,sn=0)

Arguments

Value

returns the generated realization

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

gen.arch.wge(n=200,alpha0=.1,alpha=c(.36,.27,.18,.09))

Function to generate an ARIMA (or ARMA) realization

Description

This function calls arima.sim but with more simple parameter structure for stationary ARIMA (or ARMA) models

Usage

gen.arima.wge(n, phi=0, theta=0, d=0,s=0,mu=0,vara=1,plot=TRUE,sn=0)

Arguments

Value

This function simply generates and (optionally plots) an ARIMA (or ARMA) realization

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

gen.arima.wge(n=100, phi=c(1.6,-.9), theta=.8, d=1, vara=1, plot=TRUE)

Function to generate an ARMA realization

Description

This function calls arima.sim but with more simple parameter structure for stationary ARMA models

Usage

gen.arma.wge(n, phi=0, theta=0, mu=0,vara = 1,plot = TRUE,sn=0)

Arguments

Value

This function simply generates and (optionally plots) an ARMA realization

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

gen.arma.wge(n=100, phi=c(1.6,-.9), theta=.8, mu=50,vara=1, plot=TRUE)

Function to generate an ARUMA (or ARMA or ARIMA) realization

Description

This function calls arima.sim but an a similar manner to gen.ns.arma.wge and gen.ns.arima.wge but allows for generation of realizations from ARUMA models (see Chapter 5 of "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

gen.aruma.wge(n,phi=0,theta=0,d=0,s=0,lambda=0,vara=1,plot=TRUE,sn=0)

Arguments

Value

This function generates and (optionally plots) an ARMA or ARIMA or ARUMA realization

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

gen.aruma.wge(n=100,phi=.7,theta=0, d=1, s=4,lambda=c(1.8,-1),vara=1, plot=TRUE)

Generate a realization from a GARCH(p0,q0) model

Description

Generates a realization of length n from the GARCH(p0,q0) model (4.26) in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

gen.garch.wge(n,alpha0,alpha,beta,plot=TRUE,sn=0)

Arguments

Value

returns the generated realization

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

gen.garch.wge(n=200,alpha0=.1,alpha=.45,beta=.45)

Function to generate a GARMA realization

Description

This function calls gen.geg.wge and arima.sim

Usage

gen.garma.wge(n,u,lambda,phi = 0,theta=0,trun=300000,burn_in=600,vara=1,plot=TRUE,sn=0)

Arguments

Value

This function generates and (optionally plots) an GARMA realization

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

gen.garma.wge(n=100, u=.8,lambda=.4,phi=.9)

Function to generate a Gegenbauer realization

Description

This function calls macoef.wge

Usage

gen.geg.wge(n, u, lambda, trun = 300000,  vara=1 ,sn = 0)

Arguments

Details

This function is called by gen.garma.wge and does not have a burn-in time. Thus, we recommend using est.garma.wge for generating realizations from a Gegenbauer model.

Value

This function generates a Gegenbauer realization

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

gen.geg.wge(n=100, u=.8,lambda=.4)

Function to generate a g(lambda) realization

Description

This function generates a g(lambda) TVF realization as discussed in Chapter 13 of Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Usage

gen.glambda.wge(n, lambda, phi =0, offset = 20, vara = 1, plot = TRUE, sn = 0)

Arguments

Value

This function simply generates and (optionally plots) an ARMA realization

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

gen.glambda.wge(n=500, lambda=0.5,phi=c(1.9,-.99), vara=1, plot=TRUE,sn=0)

Generate data from a signal-plus-noise model

Description

Generate a realization from the model x(t)=coef[1]*cos(2*pi*freq[1]*t+psi[1])+coef[2]*cos(2*pi*freq[2]*t+psi[2])+a(t)

Usage

gen.sigplusnoise.wge(n,b0,b1=0,coef,freq,psi,phi=0,vara=1,plot=TRUE,sn=0)

Arguments

Value

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

x=gen.sigplusnoise.wge(n=100,coef=c(3,1),freq=c(.1,.4),psi=c(0,0),vara=2)

Global Temperature Data: 1850-2009

Description

Annual temperature anomalies from the average for the years 1850-2009

Usage

data("global.temp")

Format

The format is: List of 2 $ year : num [1:160] 1850 1851 1852 1853 1854 ... $ annual: num [1:160] -0.447 -0.292 -0.294 -0.337 -0.307 -0.321 -0.406 -0.503 -0.513 -0.349 ...

Source

Climatic Research Unit at East Anglia, England, in conjunction with the Met Office Hadley Centre

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(global.temp)

Global Temperature Data: 1880-2009

Description

Annual temperature anomalies from the average for the years 1850-2009

Usage

data("global.temp")

Format

The format is: ts file containing annual temperatures from 1880 through 2020

Source

ncdc.noaa.gov

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(global2020)

Global temperature data

Description

Global temperature data for 1850-2009. The data are temperature anomalies, i.e. departures from the average for 1850-2009

Usage

data("hadley")

Format

The format is: num [1:160] -0.447 -0.292 -0.294 -0.337 -0.307 -0.321 -0.406 -0.503 -0.513 -0.349 ...

Source

Met Office Hadley Centre

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(hadley)

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

Description

Function is used with the tswge function wv.wge

Usage

hilbert.wge(input)

Arguments

Value

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(airline)
hilbert.wge(airline)

Instantaneous spectrum

Description

Calculates instantaneous spectrum (in dB) based on a G(lambda) time transformation

Usage

is.glambda.wge(n, phi = 0, sigma2 = 1, lambda, offset)

Arguments

Value

Simply a plot of the realization

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

is.glambda.wge(n=200,phi=c(.93,-.32,-.15,-.15,-.17),lambda=-.4,offset=63)

Sample instantaneous spectrum based on periodogram

Description

Calculates sample instantaneous spectrum (in dB) based on a G(lambda) time transformation

Usage

is.sample.wge(data,  lambda, offset)

Arguments

Value

Simply a plot of the realization

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(ss08)
is.sample.wge(data=ss08,lambda=-.4,offset=63)

Kalman filter for simple signal plus noise model with missing data

Description

Kalman function to predict, filter, and smooth in the presence of missing data; see Section 10.6 4 in Applied Time Series Analysis with R

Usage

kalman.miss.wge(y,start, gam0, F, gamV, Gtmiss, gamW)

Arguments

Value

Note

Calls Ksmooth1 in CRAN package 'astsa'

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(table10.1.signal)
data(table10.1.noise)
spn=table10.1.signal+table10.1.noise
n=75
Gtmiss=array(1,dim=c(1,1,n))
Gtmiss[1,1,2]=0
Gtmiss[1,1,5]=0
kalman.miss.wge(y=spn,start=0,gam0=1,F=.9,gamV=1,Gtmiss,gamW=.75)

Kalman filter for simple signal plus noise model

Description

Kalman filter program to predict, filter, and smooth related to the material in Section 10.6 4 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Usage

kalman.wge(y, start, gam0, F, gamV, G, gamW)

Arguments

Value

Note

Requires CRAN package 'astsa'

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(table10.1.signal)
data(table10.1.noise)
spn=table10.1.signal+table10.1.noise
kalman.wge(y=spn,start=0,gam0=1,F=.9,gamV=1,G=1,gamW=.75)

King Kong Eats Grass

Description

Digitized record taken at 8,000 Hz of voltage readings obtained from the acoustical energy generated by Wayne Woodward speaking the words "King Kong eats grass" while a fan was blowing in the background

Usage

data("kingkong")

Format

The format is: num [1:15418] -0.001831 -0.000916 -0.003357 -0.002716 -0.000977 ...

Source

See description above

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(kingkong)

Lavon lake water levels

Description

Data given in feet above sea level. Quarterly data, 1982-2009

Usage

data("lavon")

Format

The format is: num [1:112] 495 492 500 491 492 ...

Source

http://lavon.uslakes.info/levelcal.asp

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(lavon)

Lavon Lake Levels to September 30, 2015

Description

Feet above sea level for Lavon Lake, quarterly data through September 2015. An extension of data lavon

Usage

data("lavon15")

Format

The format is: num [1:135] 495 492 500 491 492 ...

Source

Lake Data internet

Examples

data(lavon15)

Linear chirp data.

Description

256 point linear chirp data, the first 150 points of which are shown in Figure 3.16(a) Time Series Analysis for Data Science: Analysis and Forecasting by Woodward, Sadler, and Robertson

Usage

data("linearchirp")

Format

The format is: List of 2 $ x : num [1:256] 1 1 0.98 0.95 0.91 0.86 0.8 0.72 0.63 0.53 ... $ spec: num [1:256] 0.511 0.568 0.733 0.991 1.32 ...

Source

Simulated data

References

Time Series Analysis for Data Science: Analysis and Forecasting by Woodward, Sadler, and Robertson

Examples

data(linearchirp)

Ljung-Box Test

Description

Performs Ljung-Box Test for white noise

Usage

ljung.wge(x, K = 24, p = 0, q = 0)

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig1.22a)
ljung.wge(fig1.22a, K=24,p=0,q=0)

Log (base 10) of lynx data

Description

The log (base 10) of the annual number of lynx trapped in the Mackenzie River district of the North-West Canada (dataset lynx in this package)

Usage

data("llynx")

Format

The format is: Time-Series [1:114] from 1821 to 1934: 2.43 2.51 2.77 2.94 3.17 ...

Source

Tong (1977). Journal of the Royal Statistical Society A, 432-436.

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(llynx)

Lynx data

Description

The lynx data are the annual number of lynx trapped in the Mackenzie River district of Canada

Usage

data("lynx")

Format

The format is: Time-Series [1:114] from 1821 to 1934: 269 321 585 871 1475 ...

Source

Tong (1977). Journal of the Royal Statistical Society A, 432-436.

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(lynx)

Predictive or rolling moving average

Description

Given a time series in the vector x and order (either an odd or even integer) ma.pred.wge computes a predictive moving average giving 1-step ahead predictions through x(n+1). Optionally, you can specify k-step ahead forecasts beyond the end of the data.

Usage

ma.pred.wge(x,order=3,n.ahead=1,plot=TRUE)

Arguments

Value

Author(s)

Wayne Woodward

References

"Practical Time Series Analysis with R" by Woodward, Sadler, and Robertson"

Examples

data(wtcrude)
sm=ma.pred.wge(x=wtcrude,order=5,n.ahead=10)

Centered Moving Average Smoother

Description

Given a time series in the vector x and order (either an odd or even integer) ma.smooth.wge computes a centered moving average smoother and optionally plots the data and smoothed data

Usage

ma.smooth.wge(x,order=3,plot=TRUE)

Arguments

Value

Author(s)

Wayne Woodward

References

"Practical Time Series Analysis with R" by Woodward, Sadler, and Robertson"

Examples

data(wtcrude)
sm=ma.smooth.wge(x=wtcrude,order=5)

Simulated MA(2) data

Description

This realization is used to obtain the innovations estimates shown in Table 7.1

Usage

data("ma2.table7.1")

Format

The format is: num [1:400] 1.299 1.831 -0.162 -0.648 1.243 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(ma2.table7.1)

Calculate coefficients of the general linear process form of a Gegenbauer process

Description

Calculate coefficients of the general linear process form of a Gegenbauer process based on formula (8), page 6 of Ferrara and Guegan(2001).

Usage

macoef.geg.wge(u, lambda, trun = 300000)

Arguments

Details

This function is called by gen.geg.wge

Value

A vector of length trun containing the GLP coefficients

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott and Ferrara and Guegan(2001)

Examples

mageg=macoef.geg.wge(u=.8,lambda=.3)

Massachusettts Mountain Earthquake Data

Description

Lg wave from from an earthquake known as Massachusetts Mountain Earthquake(5 August 1971), which was recorded at the Mina Nevada station

Usage

data("mass.mountain")

Format

The format is: num [1:454] -0.03655 -0.01774 0.00218 0.01193 0.00915 ...

Source

Gupta, Chan, and Wagner (2005). Regional sources discrimination of small events based on the use of Lg wavetrain, Bulletin of the Seismological Society of America 95, 341-346.

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(mass.mountain)

Massachusetts Mountain Earthquake data shown in Figure 13.13a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Description

Lg wave for Massachusetts Mountain Earthquake

Usage

data("mm.eq")

Format

The format is: num [1:454] -0.03655 -0.01774 0.00218 0.01193 0.00915 ...

Source

Gupta, et al. (2005) Bulletin of the Seismological Society of America 95, 341-346.

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(mm.eq)

Multiply Factors

Description

The function multiplies the AR (or MA) factors of a model to produce the model in unfactored form. Requires the CRAN package 'PolynomF'.

Usage

mult.wge(fac1 = 0, fac2 = 0, fac3 = 0, fac4 = 0, fac5 = 0, fac6 = 0)

Arguments

Value

Note

Requires CRAN package 'PolynomF'

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

fac1=c(1.6,-.9)
fac2=.8
mult.wge(fac1,fac2)

256 noisy bumps signal

Description

Noisy bumps signal shown in Figure 12.11(a) in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Usage

data("nbumps256")

Format

The format is: num [1:256] -0.234 0.123 0.303 0.134 -0.513 ...

Source

Donoho and Johnstone(1994) Biometrika 81,425-455

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(nbumps256)

Annual minimal water levels of Nile river

Description

Water levels for 622 through 1284 measured at Roda gauge near Cairo (Tousson, 1925)

Usage

data("nile.min")

Format

The format is: Time-Series [1:663] from 622 to 1284: 1157 1088 1169 1169 984 ...

Source

Tousson, O. (1925) M\'emoire sur l'Histoire du Nil, Volume 18 in M\'emoires a l'Institut d'Egypte, pp. 366-404.

References

Beran, J. (1994) Statistics for Long-Memory Processes, Chapman Hall: Englewood, NJ.

Examples

data(nile.min)

Nyctalus noctula echolocation data

Description

Echolocation signal for the Nyctalus noctula hunting bat

Usage

data("noctula")

Format

The format is: num [1:96] -18 16 -5 -17 21 -6 -17 20 -6 -16 ...

Source

Internet

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Daily Number of Chicken-Fried Steaks Sold

Description

Daily number of chicken-fried steaks sold at Ozona Bar and Grill during June and July 2019

Usage

data("ozona")

Format

ts object consisting of number of chicken fried steaks sold daily during June and July, 2019

Source

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(ozona)

Compute partial autocorrelations

Description

Compute partial autocorrelations using either YW (default and the classical method), Burg, or ML estimates.)

Usage

pacfts.wge(x,lag.max=5, plot=TRUE,na.action,limits=FALSE,method ='yw')

Arguments

Value

Author(s)

Wayne Woodward

References

"Time Series for Data Science: Analysis and Forecasting with R" by Woodward, Sadler, and Gray

Examples

data(sunspot2.0)
          pacfts.wge(sunspot2.0,lag.max=10,method='burg')

Smoothed Periodogram using Parzen Window

Description

This function calculates and optionally plots the smoothed periodogram using the Parzen window. The truncation point may be chosen by the user

Usage

parzen.wge(x, dbcalc = "TRUE", trunc = 0, plot = "TRUE")

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

parzen.wge(rnorm(100))

Pennsylvania average monthly temperatures

Description

Pennsylvania average monthly temperatures

Usage

data("patemp")

Format

The format is: num [1:180] 38.1 38.3 44.5 52.3 59.2 70.6 73.9 71.3 63.9 57.3 ...

Source

Internet

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and elliott

Examples

data(patemp)

Calculate the periodogram

Description

Given a realization contained in a vector, this function calculates and optionally plots the periodogram in either log or non-log scale

Usage

period.wge(x, dbcalc = "TRUE", plot = "TRUE")

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

period.wge(rnorm(100))

Calculate pi weights for an ARMA model

Description

Given the coefficients of the AR and MA parts of an ARMA model, this function calculates the pi weights

Usage

pi.weights.wge(phi = 0, theta = 0, lag.max =5)

Arguments

Value

A vector containing pi(1), ..., pi(lag.max)

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

pi.weights.wge(phi=c(1.2,-.6), theta=.5, lag.max=5)

Plots Discrete Wavelet Transform (DWT)

Description

Plots DWT obtained using functiond dwt from waveslim

Usage

plotts.dwt.wge(x, n.levels, type='S8')

Arguments

Details

The wavelsim dwt function names these :'haar', 'la8','d4','d6',and 'd8' respectively and the conversion is done transparently within the R code. This is done transparently within the R code.

Value

The output is a plot of the DWT.

Note

Requires CRAN package 'waveslim'

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(bumps256)
                   plotts.dwt.wge(bumps256,n.levels=4,type='S8')

Plots MRA plot)

Description

Plots MAR ;plot associated with a multiresolution analysis using function mra from waveslim

Usage

plotts.mra.wge(x, n.levels, type='S8')

Arguments

Details

The wavelsim mra function names these :'haar', 'la8','d4','d6',and 'd8' respectively and the conversion is done transparently within the R code. This is done transparently within the R code.

Value

The output is a plot of the MRA.

Note

Requires CRAN package 'waveslim'

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(bumps256)
                   plotts.mra.wge(bumps256,n.levels=4,type='S8')

Calculate and plot the periodogram and Parzen window estimates with differing trunctaion points

Description

Given a time series contained in the vector x, plotsp.parzen.wge calculates and plots the periodogram and Parzen window estimates at the default truncation point M=2*sqrt(n) and up to 2 additional user specified trunctaion points.

Usage

plotts.parzen.wge(x, m2=c(0,0))

Arguments

Details

m2=c(10,24) indicates that in addition to the default truncation point, the smoothed spectral estimator is to be calculated using truncation points 10 and 24, m2=c(0,0) indicates that no additional truncation points are to be used, and m2=c(10,0) indicates the use of one additional truncation point (10)

Value

Author(s)

Wayne Woodward

References

"Applied Time series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(ss08)
    m2=c(10,50)
    plotts.parzen.wge(ss08,m2)

Plot Data, Sample Autocorrelations, Periodogram, and Parzen Spectral Estimate

Description

For a given realization, this function plots the data, and calculates and plots the sample autocorrelations, periodogram, and Parzen window spectral estimator in a 2x2 array of plots.

Usage

plotts.sample.wge(x, lag.max = 25, trunc = 0, arlimits=FALSE,speclimits=c(0,0),
periodogram=FALSE)

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(wages)
plotts.sample.wge(wages,trunc=0)

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

Description

For a given ARMA model, this function plots a realization, the true autocorrelations, and the true spectral density. This plot is typical of many plots in Applied Time Series Analysis by Woodward, Gray, and Elliott. For example, see Figure 1.21 and Figure 3.23.

Usage

plotts.true.wge(n=100, phi=0, theta=0, lag.max=25, mu=0,vara = 1,sn=0,plot.data=TRUE)

Arguments

Value

Note

gvar=g[1], i.e. autocovariance at lag 0

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

plotts.true.wge(n=100, phi=c(1.6,-.9), theta=.8, lag.max=25, vara = 1) 

Plot a time series realization

Description

Given a realization contained in a vector, this function plots it as a time series realization

Usage

plotts.wge(x,style = 0, xlab = "Time", ylab = "",main="",col='black',text_size=12,
lwd=0.75,cex=0.5,cex.lab=0.75,cex.axis=0.75,xlim=NULL,ylim=NULL)

Arguments

Value

Simply a plot of the realization

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(sunspot2.0);plotts.wge(sunspot2.0)

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

Description

Matrix containing a bivariate VAR data set

Usage

data("prob10.4")

Format

The format is: num [1:100, 1:2] 0 0.7184 -0.3448 -2.1638 -0.0342 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:2] "X1" "X2"

Source

Simulated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(prob10.4)

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

Description

This realization is the unobservable data associated with the observed data in prob10.6y

Usage

data("prob10.6x")

Format

The format is: num [1:9] 2.61 0.69 0.64 0.37 -0.79 -1.63 -1.14 -1.2 -3.13

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(prob10.6x)

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

Description

Kalman filter example data

Usage

data("prob10.6y")

Format

The format is: num [1:9] 3.28 -0.05 0.64 0.31 -0.9 -2.4 -1.83 -1.93 -3.52

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(prob10.6y)

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

Description

This realization is the same unobservable data as in prob10.6x

Usage

data("prob10.7x")

Format

The format is: num [1:9] 2.61 0.69 0.64 0.37 -0.79 -1.63 -1.14 -1.2 -3.13

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(prob10.7x)

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

Description

Kalman filter example data

Usage

data("prob10.7y")

Format

The format is: num [1:9] 3.28 -0.05 0.64 0.31 -0.9 -2.4 -1.83 -1.93 -3.52

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(prob10.7y)

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

Description

Simulated fractional long memory data

Usage

data("prob11.5")

Format

The format is: num [1:10] 4.2 -2.5 8.4 14.6 7 9.6 19.8 4.8 6.5 8.3

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(prob11.5)

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

Description

Data from a problem set in the wavelet chapter

Usage

data("prob12.1c")

Format

The format is: num [1:200] 9.49 8.01 3.43 -1.85 -4.99 -7.21 -5.61 -2.34 2.16 3.88 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(prob12.1c)

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

Description

Data from a problem set in the wavelet chapter

Usage

data("prob12.3a")

Format

The format is: num [1:512] -3.09 8.43 -9.74 8.44 -3.46 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(prob12.3a)

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

Description

Data from a problem set in the wavelet chapter

Usage

data("prob12.3b")

Format

The format is: num [1:256] 1 1 1 1 1 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(prob12.3b)

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

Description

Simulated TVF data set

Usage

data("prob12.6c")

Format

The format is: num [1:512] -0.482 -0.569 -0.656 -0.743 -0.83 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(prob12.6c)

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

Description

Simulated data from cosine-plus-noise model

Usage

data("prob13.2")

Format

The format is: num [1:256] 1.524 5.886 5.939 4.319 0.573 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(prob13.2)

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

Description

See title above

Usage

data("prob8.1a")

Format

The format is: num [1:200] 2.19 0.48 0.06 3.86 3.6 -3.38 6.23 1.95 1.4 -5.35 ...

Source

Simulated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(prob8.1a)

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

Description

See title above

Usage

data("prob8.1b")

Format

The format is: num [1:200] 1.54 -0.13 1.93 0.29 -0.13 -0.23 1.27 1.01 -0.65 1.68 ...

Source

Simulated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(prob8.1b)

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

Description

See title above

Usage

data("prob8.1c")

Format

The format is: num [1:200] 0.33 -0.53 -2.36 2.48 -0.36 -2.02 1.87 -0.73 0.41 2.41 ...

Source

Simulated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(prob8.1c)

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

Description

See title above

Usage

data("prob8.1d")

Format

The format is: num [1:200] -0.07 -1.74 -1.37 -0.52 0.14 0.07 -1.5 1.88 -0.03 -1.81 ...

Source

Simulated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(prob8.1d)

Data set 1 for Problem 6.1c

Description

Data set 1 for Problem 6.1c in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott. It is either from line plus noise or random walk with drift.

Usage

data("prob9.6c1")

Format

The format is: num [1:100] -0.2924 0.0206 0.6595 0.3819 0.0269 ...

Source

Simulated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(prob9.6c1)

Data set 2 for Problem 6.1c

Description

Data set 2 for Problem 6.1c in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott. It is either from line plus noise or random walk with drift.

Usage

data("prob9.6c2")

Format

The format is: num [1:100] -0.925 -2.679 -2.378 -3.03 -2.157 ...

Source

Simulated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott.

Examples

data(prob9.6c2)

Data set 3 for Problem 6.1c

Description

Data set 3 for Problem 6.1c in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott. It is either from line plus noise or random walk with drift.

Usage

data("prob9.6c3")

Format

The format is: num [1:100] -2.79 -3.32 -3.51 -5.13 -3.51 ...

Source

Simulated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(prob9.6c3)

Data set 4 for Problem 6.1c

Description

Data set 4 for Problem 6.1c in "Applied Time Series and Data Analysis with R, 2nd edition" by Woodward, Gray, and Elliott. It is either from line plus noise or random walk with drift.

Usage

data("prob9.6c4")

Format

The format is: num [1:100] -0.0599 -0.0214 0.6589 -0.151 0.4043 ...

Source

Simulated data

References

"Applied Time Series and Data Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(prob9.6c4)

Calculate psi weights for an ARMA model

Description

Given the coefficients of the AR and MA parts of an ARMA model, this function calculates the psi weights

Usage

psi.weights.wge(phi = 0, theta = 0, lag.max = 5)

Arguments

Value

A vector containing psi(1), ..., psi(lag.max)

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

psi.weights.wge(phi=c(1.2,-.6), theta=.5, lag.max=5)

Daily DOW rate of Return

Description

Daily DOW rate of return from 1971 through 2020

Usage

data("rate")

Format

ts object consisting of daily dow rate of return from 1971 through 2020

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(rate)

Function to Calculate the Rolling Window RMSE

Description

This function creates as many "windows" as is possible with the data and calculates an RMSE for each window. The resulting "rolling window RMSE" is the average of the individual RMSEs from each window.

Usage

roll.win.rmse.nn.wge(series, horizon = 1, fit_model)

Arguments

Value

Author(s)

Bivin Sadler

References

"The Time Series Tool Kit"

Function to Calculate the Rolling Window RMSE

Description

This function creates as many "windows" as is possible with the data and calculates an RMSE for each window. The resulting "rolling window RMSE" is the average of the individual RMSEs from each window.

Usage

roll.win.rmse.wge(series, horizon = 2, s = 0, d = 0, phi = 0, theta = 0)

Arguments

Value

Author(s)

Bivin Sadler

References

"The Time Series Tool Kit"

Simple Linear Regression

Description

Uses Base R routine lm to simplify call for SLR where independent variable is automatocally t=1:n

Usage

slr.wge(x)

Arguments

Value

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

x=gen.arma.wge(n=100,phi=.96,sn=10)
                   y=slr.wge(x)

Sunspot Data

Description

Annual average sunspot numbers for the years 1749-2008

Usage

data("ss08")

Format

The format is: num [1:260] 80.9 83.4 47.7 47.8 30.7 ...

Source

Internet-open source

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(ss08)

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

Description

Sunspot data from 1850 through 2008 for matching with global temperature data (hadley) for purposes of testing for association in Example 10.5 of "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Usage

data("ss08.1850")

Format

The format is: num [1:160] 66.6 64.5 54.1 39 20.6 ...

Source

Internet

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(ss08.1850)

Starwort Explosion data shown in Figure 13.13a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Description

Lg wave for Starwort explosion data

Usage

data("starwort.ex")

Format

The format is: num [1:420] 43245 48408 47565 7372 -62277 ...

Source

Gupta, et al. (2005) Bulletin of the Seismological Society of America 95, 341-346.

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(starwort.ex)

Classic Sunspot Data: 1749-1924

Description

The classic 176 point sunspot data from 1749-1924 that has been widely modeled

Usage

data("sunspot.classic")

Format

The format is: num [1:176] 80.9 83.4 47.7 47.8 30.7 12.2 9.6 10.2 32.4 47.6 ...

Source

Internet

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(sunspot.classic)

Annual Sunspot2.0 Numbers

Description

Annual sunspot2.0 numbers from 1700 through 2020

Usage

data("sunspot2.0")

Format

ts object consisting of annual data from 1700 through 2020

Source

https://www.sidc.oma.be/silso

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(sunspot2.0)

Monthly Sunspot2.0 Numbers

Description

Monthly sunspot2.0 numbers from January 1749 through December 2020

Usage

data("sunspot2.0.month")

Format

ts object consisting of monthly data from January 1749 through December 2020

Source

https://www.sidc.oma.be/silso

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(sunspot2.0.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 Elliott

Description

The data in Table 10.1 are of the form Y(t)=X(t)+n(t). This data set contains the values for n(t).

Usage

data("table10.1.noise")

Format

The format is: num [1:75] -0.49 0.126 -0.129 -1.179 0.441 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(table10.1.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 Elliott

Description

The X(t) data is unobservable, and is a realization from an AR(1) model

Usage

data("table10.1.signal")

Format

The format is: num [1:75] -0.2497 -0.0812 -0.6463 -1.7653 -2.719 ...

Source

Simulated data

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(table10.1.signal)

MA(2) data for Table 7.1

Description

MA(2) data for Table 7.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott. Uses function ia in package itsmr to show steps in the innovations algorithm for estimating the MA parameters and white noise variance

Usage

data("table7.1")

Format

The format is: num [1:400] 0.4481 0.5497 -1.6586 -3.1653 -0.0314 ...

Source

Generated data

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(table7.1)

Tesla Stock Prices

Description

Teslas daily stock prices from January 1, 2020 through April 30, 2021

Usage

data("tesla")

Format

ts object consisting of daily adjusted close price for TSLA from January 1, 2020 through April 30, 2021

Source

https://finance.yahoo.com

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(tesla)

Transforms TVF data set to a dual data set

Description

Using the specified values for lambda and offset, this function transforms a TVF data set to a dual data set based on a Glambda time transformation.

Usage

trans.to.dual.wge(x, lambda, offset = 60, h = 0, plot = TRUE)

Arguments

Value

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig13.2c)
                   y=trans.to.dual.wge(x=fig13.2c,lambda=-.4,offset=63)

Transforms dual data set back to original time scale

Description

Using the specified values for lambda and offset, this function transforms a dual data set, based on a Glambda time transformation, back to the original time scale

Usage

trans.to.original.wge(xd, lambda, offset, h, plot = TRUE)

Arguments

Value

Returns the y values to be plotted at time points t=1 to n that approximate the original TVF data set

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(fig13.2c)
                   yd=trans.to.dual.wge(fig13.2c,lambda=-.4,offset=63)
                   yo=trans.to.original.wge(yd$intY,lambda=-.4,offset=63,h=yd$h)

True ARMA autocorrelations

Description

R function to calculate the autocovariances and autocorrelations and optionally plot the true autocorrelations of a stationary ARMA model

Usage

true.arma.aut.wge(phi = 0, theta = 0, lag.max = 25, vara = 1,plot=TRUE)

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

true.arma.aut.wge(phi=c(1.6,-.9),theta=-.8,lag.max=15,vara=1) 

True ARMA Spectral Density

Description

R function to calculate and optionally plot the spectral density of a stationary ARMA model

Usage

true.arma.spec.wge(phi=0,theta=0, vara=1,plot=TRUE)

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

true.arma.spec.wge(phi=c(1.6,-.9), theta=.7) 

True FARMA autocorrelations

Description

Calculate the autocovariances and autocorrelations and optionally plot the true autocorrlations of a FARMA model

Usage

true.farma.aut.wge(d,phi=0,theta=0,lag.max=50,trunc=1000,vara=1,plot=TRUE)

Arguments

Details

For fractional model use phi=theta=0

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, second editon" by Woodward, Gray, and Elliott

Examples

y=true.farma.aut.wge(d=.4,phi=c(0,-.8))

True GARMA autocorrelations

Description

Calculate the autocovariances and autocorrelations and optionally plot the true autocorrelations of a 1-factor based on formula(11.25) of "Applied Time Series Analysis with R, second editon" Woodward, Gray, and Elliott

Usage

true.garma.aut.wge(u,lambda,phi=0,theta=0,lag.max=50,vara=1,plot=TRUE)

Arguments

Details

For Gegenbauer model use phi=theta=0

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, second editon" by Woodward, Gray, and Elliott

Examples

y=true.garma.aut.wge(u=.8,lambda=.4,phi=.8)

Texas Seasonally Adjusted Unnemployment Rates

Description

Monthly seasonally adjusted unemployment rate in Texas for the years 2000-2019

Usage

data("tx.unemp.adj")

Format

ts object consisting of monthly seasonally adjusted unemployment rate from January 2000 through December 2019

Source

https://twc.texas.gov

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(tx.unemp.adj)

Texas Unadjusted Unnemployment Rates

Description

Monthly unemployment rate in Texas for the years 2000-2019

Usage

data("tx.unemp.unadj")

Format

ts object consisting of monthly unadjusted unemployment rate from January 2000 through December 2019

Source

https://twc.texas.gov

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(tx.unemp.unadj)

Plot the roots of the characteristic equation on the complex plain.

Description

This function plots the roots of the characteristic equation on the complex plain and super imposes the Unit Circle to show if a root is inside, outside or on the Unit Circle. The modulus and absolule reciprical are also displayed.

Usage

unit.circle.wge(real = 0, imaginary = 0)

Arguments

Value

returns a plot of the root with respect to the unit circle

Author(s)

Bivin Sadler

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

unit.circle.wge(real = .9, imaginary = .95)

Quarterly US Retail Sales

Description

Quarterly US retail sales (in $millions) from the fourth quarter of 1999 through the second quarter of 2021

Usage

data("us.retail")

Format

ts object consisting of quarterly US retail sales (in $millions) from the fourth quarter of 1999 through the second quarter of 2021

Source

https://www.fred.stlouis.org

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(us.retail)

US population

Description

US estimated annual population from 1900 through 2020.

Usage

data("uspop")

Format

ts object consisting of annual data from 1700 through 2020

Source

Internet

References

"Time Series for Data Science: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Daily wages in Pounds from 1260 to 1944 for England

Description

This data set contains the average English daily wages in pounds for each year from 1260 to 1944, inclusive.

Usage

data("wages")

Format

The format is: num [1:735] 4.41 4.63 4.38 4.52 4.42 4.64 4.44 5.15 5.23 4.42 ...

Source

Data Market Time Series Data Library (citing: Makridakis, Wheelwright and Hyndman (1998))

Examples

data(wages)

Woodward-Bottone-Gray test for trend

Description

Performs the Woodward-Bottone-Gray (WBG) bootstrap-based test for a linear trend in a time series realization.)

Usage

wbg.boot.wge(x,nb=399,alpha=.05,pvalue=TRUE,sn=0)

Arguments

Value

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(global.temp)
          wbg.boot.wge(global.temp)

Whale click data

Description

256 point whale click echolocation signal

Usage

data("whale")

Format

The format is: num [1:286] 0.0014 -0.008 0.01126 0.00412 0.0069 ...

Source

Stan Kuczaj from University of Southern Mississippi

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(whale)

West Texas Intermediate Crude Oil Prices

Description

Monthly West Texas intermediate crude oil prices from January 2000 through October 2009.

Usage

data("wtcrude")

Format

The format is: num [1:118] 27.2 29.4 29.9 25.7 28.8 ...

Source

Internet

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Monthly WTI Crude Oil Prices

Description

Monthly WTI crude oil prices from January 1990 through December 2020

Usage

data("wtcrude2020")

Format

ts object consisting of monthly data from January 1990 through December 2020

Source

https://fred.stlouis.org

References

"Time Series for Data Sience: Analysis and Forecasting" by Woodward, Sadler, and Robertson

Examples

data(wtcrude2020)

Function to calculate Wigner Ville spectrum

Description

Calculates and plots Wigner-Ville spectrum for a realization

Usage

wv.wge(x)

Arguments

Value

Plots Wigner-Ville spectrum

Author(s)

Wayne Woodward

References

Boashash (2003). Time Frequency Analysis

Examples

data(doppler)
wv.dop=wv.wge(doppler)

Precleaned Yellow Cab data

Description

The number of Yellow Cab Trips in NYC before and during the COVID outbreak: January 2019 through February 2021

Usage

data("yellowcab.precleaned")

Format

The format is: Time-Series [1:26] from 2019 to 2021: 247315 250654 252634 247742 ...

Source

NYC Taxi and Limousine website

References

Time Series for Data Science Woodward, Sadler, and Robertson

Examples

data(yellowcab.precleaned)