tswge package - RDocumentation

bumps16

16 point bumps signal

fig10.3x1

Variable X1 for the bivariate realization shown in Figure 10.3"

fig4.8a

Gaussian White Noise

fig5.3c

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

freight

Freight data

freeze

Minimum temperature data

hadley

Global temperature data

global.temp

Global Temperature Data: 1850-2009

patemp

Pennsylvania average monthly temperatures

period.wge

Calculate the periodogram

plotts.dwt.wge

Plots Discrete Wavelet Transform (DWT)

plotts.mra.wge

Plots MRA plot)

prob8.1b

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

bumps256

256 point bumps signal

eco.mort30

30 year mortgage rates

fig1.21a

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

est.ar.wge

Estimate parameters of an AR(p) model

prob8.1c

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

fig1.16a

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

fig10.1mort

Data shown in Figure 10.1c 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

wtcrude

West Texas Intermediate Crude Oil Prices

whale

Whale click data

backcast.wge

Calculate backcast residuals

bat

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

eco.cd6

6-month rates

eco.corp.bond

Corporate bond rates

fig10.11x

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

fig10.11y

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

fig6.11a

Cyclical Data

fig6.1nf

Data in Figure 6.1 without the forecasts

fig6.8nf

Simulated seasonal data with s=12

fig8.11a

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

gen.aruma.wge

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

gen.garch.wge

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

lynx

Lynx data

aic.wge

ARMA Model Identification

chirp

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

aic5.wge

Return top 5 AIC, AICC, or BIC picks

ma2.table7.1

Simulated MA(2) data

nbumps256

256 noisy bumps signal

nile.min

Annual minimal water levels of Nile river

prob10.4

Data matrix for Problem 10.4 in "Applied Time Series Analysis with R, 2nd 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

prob10.6x

Data for Problem 10.6 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

prob8.1d

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

prob9.6c1

Data set 1 for Problem 6.1c

doppler

Doppler Data

est.glambda.wge

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

est.garma.wge

Estimate the parameters of a GARMA model.

fig3.16a

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

fig13.18a

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

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

fig8.4a

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

fig3.18a

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

artrans.wge

Perform Ar transformations

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

dow.rate

DOW Daily Rate of Return Data

doppler2

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

est.arma.wge

Function to calculate ML estimates of parameters of stationary ARMA models

fig1.10c

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

est.farma.wge

Estimate the parameters of a FARMA model.

fig1.10d

Simulated data in Figure 1.10d 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

fore.farma.wge

Forecast using a FARMA model

fore.garma.wge

Forecast using a GARMA model

llynx

Log (base 10) of lynx data

ljung.wge

Ljung-Box Test

plotts.parzen.wge

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

fig3.10d

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

fig13.2c

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

fig6.6nf

Data in Figure 6.6 without the forecasts

gegenb.wge

Calculates Gegenbauer polynomials

fig6.7nf

Data in Figure 6.2 without the forecasts

hilbert.wge

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

gen.arch.wge

Generate a realization from an ARCH(q0) model

is.glambda.wge

Instantaneous spectrum

kalman.miss.wge

Kalman filter for simple signal plus noise model with missing data

plotts.sample.wge

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

prob10.7y

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

is.sample.wge

Sample instantaneous spectrum based on periodogram

prob11.5

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

prob9.6c4

Data set 4 for Problem 6.1c

psi.weights.wge

Calculate psi weights for an ARMA model

wv.wge

Function to calculate Wigner Ville spectrum

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

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

prob10.6y

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

prob13.2

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

prob10.7x

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

prob8.1a

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

tswge-package

Time Series package for Woodward, Gray, and Elliott text

wages

Daily wages in Pounds from 1260 to 1944 for England

airline

Classical Airline Passenger Data

dow1000

Dow Jones daily rate of return data for 1000 days

dowjones2014

Dow Jones daily averages for 2014

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

fig6.2nf

Data in Figure 6.2 without the forecasts

fig6.5nf

Data in Figure 6.5 without the forecasts

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

gen.garma.wge

Function to generate a GARMA realization

airlog

Natural log of airline data

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

fig11.4a

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

gen.arima.wge

Function to generate an ARIMA (or ARMA) realization

fig1.5

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

fore.arma.wge

Forecast from known model

fore.glambda.wge

Forecast using a G(lambda) model

fore.aruma.wge

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

fore.sigplusnoise.wge

Forecasting signal plus noise models

gen.glambda.wge

Function to generate a g(lambda) realization

gen.sigplusnoise.wge

Generate data from a signal-plus-noise model

lavon

Lavon lake water levels

gen.geg.wge

Function to generate a Gegenbauer realization

lavon15

Lavon Lake Levels to September 30, 2015

parzen.wge

Smoothed Periodogram using Parzen Window

noctula

Nyctalus noctula echolocation data

prob12.3b

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

ss08.1850

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

ss08

Sunspot Data

fig1.22a

White noise data

gen.arma.wge

Function to generate an ARMA realization

macoef.geg.wge

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

mass.mountain

Massachusettts Mountain Earthquake Data

kalman.wge

Kalman filter for simple signal plus noise model

prob12.6c

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

table7.1

MA(2) data for Table 7.1

trans.to.dual.wge

Transforms TVF data set to a dual data set

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

kingkong

King Kong Eats Grass

mult.wge

Multiply Factors

starwort.ex

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

sunspot.classic

Classic Sunspot Data: 1749-1924

cement

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

butterworth.wge

Perform Butterworth Filter

factor.comp.wge

Create a factor table and AR components for an AR realization

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

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

factor.wge

Produce factor table for a kth order AR or MA model

fig10.1bond

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

fig10.1cd

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

plotts.true.wge

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

plotts.wge

Plot a time series realization

trans.to.original.wge

Transforms dual data set back to original time scale

true.arma.spec.wge

True ARMA Spectral Density

true.farma.aut.wge

True FARMA autocorrelations

true.arma.aut.wge

True ARMA autocorrelations

true.garma.aut.wge

True GARMA autocorrelations

tswge (version 1.0.0)

Applied Time Series Analysis

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Functions in tswge (1.0.0)