| factor.comp.wge |
Create a factor table and AR components for an AR realization |
| factor.wge |
Produce factor table for a kth order AR or MA model |
| fig1.10a |
Simulated data shown in Figure 1.10a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig1.10b |
Simulated data shown in Figure 1.10b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig1.10c |
Simulated data in Figure 1.10c in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig1.10d |
Simulated data in Figure 1.10d in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig1.16a |
Simulated data for Figure 1.16a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig1.21a |
Simulated shown in Figure 1.21a of Woodward, Gray, and Elliott text |
| fig1.22a |
White noise data |
| fig1.5 |
Simulated data shown in Figure 1.5 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig10.11x |
Simulated data shown in Figure 10.11 (solid line) in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig10.11y |
Simulated data shown in Figure 10.11 (dashed line) in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig10.1bond |
Data for Figure 10.1b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig10.1cd |
Data shown in Figure 10.1a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig10.1mort |
Data shown in Figure 10.1c in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig10.3x1 |
Variable X1 for the bivariate realization shown in Figure 10.3" |
| fig10.3x2 |
Variable X2 for the bivariate realization shown in Figure 10.3" |
| fig11.12 |
Data shown in Figure 11.12a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig11.4a |
Data shown in Figure 11.4a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig12.1a |
Simulated data with two frequencies shown in Figure 12.1a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig12.1b |
Simulated data with two frequencies shown in Figure 12.1b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig13.18a |
Simulated data shown in Figure 3.18a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig13.2c |
TVF data shown in Figure 13.2c in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig3.10d |
AR(2) Realization (1-.95)^2X(t)=a(t) |
| fig3.16a |
Figure 3.16a in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott |
| fig3.18a |
Figure 3.18a in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott |
| fig3.24a |
ARMA(2,1) realization |
| fig3.29a |
Simulated data shown in Figure 3.29a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig4.8a |
Gaussian White Noise |
| fig5.3c |
Data from Figure 5.3c in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott |
| fig6.11a |
Cyclical Data |
| fig6.1nf |
Data in Figure 6.1 without the forecasts |
| fig6.2nf |
Data in Figure 6.2 without the forecasts |
| fig6.5nf |
Data in Figure 6.5 without the forecasts |
| fig6.6nf |
Data in Figure 6.6 without the forecasts |
| fig6.7nf |
Data in Figure 6.2 without the forecasts |
| fig6.8nf |
Simulated seasonal data with s=12 |
| fig8.11a |
Data for Figure 8.11a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig8.4a |
Data for Figure 8.4a in Applied time series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig8.6a |
Data for Figure 8.6a in Applied time series Analysis with R, second edition by Woodward, Gray, and Elliott |
| fig8.8a |
Data for Figure 8.8a in Applied time series Analysis with R, second edition by Woodward, Gray, and Elliott |
| flu |
Influenza data shown in Figure 10.8 (dotted line) |
| fore.arima.wge |
Function for forecasting from known model which may have (1-B)^d and/or seasonal factors |
| fore.arma.wge |
Forecast from known model |
| fore.aruma.wge |
Function for forecasting from known model which may have (1-B)^d, seasonal, and/or other nonstationary factors |
| fore.farma.wge |
Forecast using a FARMA model |
| fore.garma.wge |
Forecast using a GARMA model |
| fore.glambda.wge |
Forecast using a G(lambda) model |
| fore.sigplusnoise.wge |
Forecasting signal plus noise models |
| freeze |
Minimum temperature data |
| freight |
Freight data |
| pacfts.wge |
Compute partial autocorrelations |
| parzen.wge |
Smoothed Periodogram using Parzen Window |
| patemp |
Pennsylvania average monthly temperatures |
| period.wge |
Calculate the periodogram |
| pi.weights.wge |
Calculate pi weights for an ARMA model |
| plotts.dwt.wge |
Plots Discrete Wavelet Transform (DWT) |
| plotts.mra.wge |
Plots MRA plot) |
| plotts.parzen.wge |
Calculate and plot the periodogram and Parzen window estimates with differing trunctaion points |
| plotts.sample.wge |
Plot Data, Sample Autocorrelations, Periodogram, and Parzen Spectral Estimate |
| plotts.true.wge |
Plot of generated data, true autocorrelations and true spectral density for ARMA model |
| plotts.wge |
Plot a time series realization |
| prob10.4 |
Data matrix for Problem 10.4 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott |
| prob10.6x |
Data for Problem 10.6 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| prob10.6y |
Simulated observed data for Problem 10.6 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| prob10.7x |
Data for Problem 10.7 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| prob10.7y |
Simulated observed data for Problem 10.6 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| prob11.5 |
Data for Problem 11.5 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| prob12.1c |
Data for Problem 12.1c and 12.3c in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| prob12.3a |
Data for Problem 12.3a in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| prob12.3b |
Data for Problem 12.3b in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| prob12.6c |
Data set for Problem 12.6(C) in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| prob13.2 |
Data for Problem 13.2 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott |
| prob8.1a |
Data for Problem 8.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott |
| prob8.1b |
Data for Problem 8.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott |
| prob8.1c |
Data for Problem 8.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott |
| prob8.1d |
Data for Problem 8.1 in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott |
| prob9.6c1 |
Data set 1 for Problem 6.1c |
| prob9.6c2 |
Data set 2 for Problem 6.1c |
| prob9.6c3 |
Data set 3 for Problem 6.1c |
| prob9.6c4 |
Data set 4 for Problem 6.1c |
| psi.weights.wge |
Calculate psi weights for an ARMA model |
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