A B C D E F G H I M N O P R S T W misc
forecast-package | forecast: Forecasting Functions for Time Series and Linear Models |
accuracy.default | Accuracy measures for a forecast model |
Acf | (Partial) Autocorrelation and Cross-Correlation Function Estimation |
arfima | Fit a fractionally differenced ARFIMA model |
Arima | Fit ARIMA model to univariate time series |
arima.errors | Errors from a regression model with ARIMA errors |
arimaorder | Return the order of an ARIMA or ARFIMA model |
as.character.Arima | Fit ARIMA model to univariate time series |
as.character.bats | BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) |
as.character.ets | Exponential smoothing state space model |
as.character.tbats | TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) |
as.data.frame.forecast | Forecasting time series |
as.data.frame.mforecast | Forecasting time series |
as.ts.forecast | Forecasting time series |
auto.arima | Fit best ARIMA model to univariate time series |
autolayer | Create a ggplot layer appropriate to a particular data type |
autolayer.forecast | Forecast plot |
autolayer.mforecast | Multivariate forecast plot |
autolayer.msts | Automatically create a ggplot for time series objects |
autolayer.mts | Automatically create a ggplot for time series objects |
autolayer.ts | Automatically create a ggplot for time series objects |
autoplot.acf | ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting |
autoplot.ar | Plot characteristic roots from ARIMA model |
autoplot.Arima | Plot characteristic roots from ARIMA model |
autoplot.bats | Plot components from BATS model |
autoplot.decomposed.ts | Plot time series decomposition components using ggplot |
autoplot.ets | Plot components from ETS model |
autoplot.forecast | Forecast plot |
autoplot.mforecast | Multivariate forecast plot |
autoplot.mpacf | ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting |
autoplot.mstl | Plot time series decomposition components using ggplot |
autoplot.msts | Automatically create a ggplot for time series objects |
autoplot.mts | Automatically create a ggplot for time series objects |
autoplot.seas | Plot time series decomposition components using ggplot |
autoplot.splineforecast | Forecast plot |
autoplot.stl | Plot time series decomposition components using ggplot |
autoplot.StructTS | Plot time series decomposition components using ggplot |
autoplot.tbats | Plot components from BATS model |
autoplot.ts | Automatically create a ggplot for time series objects |
baggedETS | Forecasting using a bagged model |
baggedModel | Forecasting using a bagged model |
bats | BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) |
bizdays | Number of trading days in each season |
bld.mbb.bootstrap | Box-Cox and Loess-based decomposition bootstrap. |
BoxCox | Box Cox Transformation |
BoxCox.lambda | Automatic selection of Box Cox transformation parameter |
Ccf | (Partial) Autocorrelation and Cross-Correlation Function Estimation |
checkresiduals | Check that residuals from a time series model look like white noise |
coef.ets | Exponential smoothing state space model |
croston | Forecasts for intermittent demand using Croston's method |
CV | Cross-validation statistic |
CVar | k-fold Cross-Validation applied to an autoregressive model |
dm.test | Diebold-Mariano test for predictive accuracy |
dshw | Double-Seasonal Holt-Winters Forecasting |
easter | Easter holidays in each season |
ets | Exponential smoothing state space model |
findfrequency | Find dominant frequency of a time series |
fitted.ar | h-step in-sample forecasts for time series models. |
fitted.ARFIMA | h-step in-sample forecasts for time series models. |
fitted.Arima | h-step in-sample forecasts for time series models. |
fitted.bats | h-step in-sample forecasts for time series models. |
fitted.ets | h-step in-sample forecasts for time series models. |
fitted.forecast_ARIMA | h-step in-sample forecasts for time series models. |
fitted.modelAR | h-step in-sample forecasts for time series models. |
fitted.nnetar | h-step in-sample forecasts for time series models. |
fitted.tbats | h-step in-sample forecasts for time series models. |
forecast.ar | Forecasting using ARIMA or ARFIMA models |
forecast.Arima | Forecasting using ARIMA or ARFIMA models |
forecast.baggedModel | Forecasting using a bagged model |
forecast.bats | Forecasting using BATS and TBATS models |
forecast.default | Forecasting time series |
forecast.ets | Forecasting using ETS models |
forecast.forecast_ARIMA | Forecasting using ARIMA or ARFIMA models |
forecast.fracdiff | Forecasting using ARIMA or ARFIMA models |
forecast.HoltWinters | Forecasting using Holt-Winters objects |
forecast.lm | Forecast a linear model with possible time series components |
forecast.mlm | Forecast a multiple linear model with possible time series components |
forecast.modelAR | Forecasting using user-defined model |
forecast.mts | Forecasting time series |
forecast.nnetar | Forecasting using neural network models |
forecast.stl | Forecasting using stl objects |
forecast.stlm | Forecasting using stl objects |
forecast.StructTS | Forecasting using Structural Time Series models |
forecast.tbats | Forecasting using BATS and TBATS models |
forecast.ts | Forecasting time series |
fortify.ts | Automatically create a ggplot for time series objects |
fourier | Fourier terms for modelling seasonality |
fourierf | Fourier terms for modelling seasonality |
gas | Australian monthly gas production |
GeomForecast | Forecast plot |
geom_forecast | Forecast plot |
getResponse | Get response variable from time series model. |
getResponse.ar | Get response variable from time series model. |
getResponse.Arima | Get response variable from time series model. |
getResponse.baggedModel | Get response variable from time series model. |
getResponse.bats | Get response variable from time series model. |
getResponse.default | Get response variable from time series model. |
getResponse.fracdiff | Get response variable from time series model. |
getResponse.lm | Get response variable from time series model. |
getResponse.mforecast | Get response variable from time series model. |
getResponse.tbats | Get response variable from time series model. |
ggAcf | ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting |
ggCcf | ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting |
gghistogram | Histogram with optional normal and kernel density functions |
gglagchull | Time series lag ggplots |
gglagplot | Time series lag ggplots |
ggmonthplot | Create a seasonal subseries ggplot |
ggPacf | ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting |
ggseasonplot | Seasonal plot |
ggsubseriesplot | Create a seasonal subseries ggplot |
ggtaperedacf | ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting |
ggtaperedpacf | ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting |
ggtsdisplay | Time series display |
gold | Daily morning gold prices |
holt | Exponential smoothing forecasts |
hw | Exponential smoothing forecasts |
InvBoxCox | Box Cox Transformation |
is.acf | Is an object a particular model type? |
is.Arima | Is an object a particular model type? |
is.baggedModel | Is an object a particular model type? |
is.bats | Is an object a particular model type? |
is.constant | Is an object constant? |
is.ets | Is an object a particular model type? |
is.forecast | Is an object a particular forecast type? |
is.mforecast | Is an object a particular forecast type? |
is.modelAR | Is an object a particular model type? |
is.nnetar | Is an object a particular model type? |
is.nnetarmodels | Is an object a particular model type? |
is.splineforecast | Is an object a particular forecast type? |
is.stlm | Is an object a particular model type? |
ma | Moving-average smoothing |
meanf | Mean Forecast |
mforecast | Forecasting time series |
modelAR | Time Series Forecasts with a user-defined model |
modeldf | Compute model degrees of freedom |
monthdays | Number of days in each season |
mstl | Multiple seasonal decomposition |
msts | Multi-Seasonal Time Series |
na.interp | Interpolate missing values in a time series |
naive | Naive and Random Walk Forecasts |
ndiffs | Number of differences required for a stationary series |
nnetar | Neural Network Time Series Forecasts |
nsdiffs | Number of differences required for a seasonally stationary series |
ocsb.test | Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots |
Pacf | (Partial) Autocorrelation and Cross-Correlation Function Estimation |
plot.ar | Plot characteristic roots from ARIMA model |
plot.Arima | Plot characteristic roots from ARIMA model |
plot.bats | Plot components from BATS model |
plot.ets | Plot components from ETS model |
plot.forecast | Forecast plot |
plot.mforecast | Multivariate forecast plot |
plot.splineforecast | Forecast plot |
plot.tbats | Plot components from BATS model |
print.ARIMA | Fit ARIMA model to univariate time series |
print.baggedModel | Forecasting using a bagged model |
print.bats | BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) |
print.CVar | k-fold Cross-Validation applied to an autoregressive model |
print.ets | Exponential smoothing state space model |
print.forecast | Forecasting time series |
print.mforecast | Forecasting time series |
print.modelAR | Time Series Forecasts with a user-defined model |
print.msts | Multi-Seasonal Time Series |
print.naive | Naive and Random Walk Forecasts |
print.nnetar | Neural Network Time Series Forecasts |
print.nnetarmodels | Neural Network Time Series Forecasts |
print.OCSBtest | Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots |
print.tbats | TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) |
remainder | Extract components from a time series decomposition |
residuals.ar | Residuals for various time series models |
residuals.ARFIMA | Residuals for various time series models |
residuals.Arima | Residuals for various time series models |
residuals.bats | Residuals for various time series models |
residuals.ets | Residuals for various time series models |
residuals.forecast | Residuals for various time series models |
residuals.forecast_ARIMA | Residuals for various time series models |
residuals.nnetar | Residuals for various time series models |
residuals.stlm | Residuals for various time series models |
residuals.tbats | Residuals for various time series models |
residuals.tslm | Residuals for various time series models |
rwf | Naive and Random Walk Forecasts |
seasadj | Seasonal adjustment |
seasadj.decomposed.ts | Seasonal adjustment |
seasadj.mstl | Seasonal adjustment |
seasadj.seas | Seasonal adjustment |
seasadj.stl | Seasonal adjustment |
seasadj.tbats | Seasonal adjustment |
seasonal | Extract components from a time series decomposition |
seasonaldummy | Seasonal dummy variables |
seasonaldummyf | Seasonal dummy variables |
seasonplot | Seasonal plot |
ses | Exponential smoothing forecasts |
simulate.ar | Simulation from a time series model |
simulate.Arima | Simulation from a time series model |
simulate.ets | Simulation from a time series model |
simulate.fracdiff | Simulation from a time series model |
simulate.lagwalk | Simulation from a time series model |
simulate.modelAR | Simulation from a time series model |
simulate.nnetar | Simulation from a time series model |
simulate.tbats | Simulation from a time series model |
sindexf | Forecast seasonal index |
snaive | Naive and Random Walk Forecasts |
splinef | Cubic Spline Forecast |
StatForecast | Forecast plot |
stlf | Forecasting using stl objects |
stlm | Forecasting using stl objects |
subset.msts | Subsetting a time series |
subset.ts | Subsetting a time series |
summary.Arima | Fit ARIMA model to univariate time series |
summary.ets | Exponential smoothing state space model |
summary.forecast | Forecasting time series |
summary.mforecast | Forecasting time series |
taperedacf | (Partial) Autocorrelation and Cross-Correlation Function Estimation |
taperedpacf | (Partial) Autocorrelation and Cross-Correlation Function Estimation |
taylor | Half-hourly electricity demand |
tbats | TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) |
tbats.components | Extract components of a TBATS model |
thetaf | Theta method forecast |
trendcycle | Extract components from a time series decomposition |
tsclean | Identify and replace outliers and missing values in a time series |
tsCV | Time series cross-validation |
tsdiag.ets | Exponential smoothing state space model |
tsdisplay | Time series display |
tslm | Fit a linear model with time series components |
tsoutliers | Identify and replace outliers in a time series |
window.msts | Multi-Seasonal Time Series |
wineind | Australian total wine sales |
woolyrnq | Quarterly production of woollen yarn in Australia |
`[.msts` | Multi-Seasonal Time Series |