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 |