# Forecasting Functions for Time Series and Linear Models

## Documentation for package ‘forecast’ version 8.21

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

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

## -- A --

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

## -- B --

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

## -- C --

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

## -- D --

dm.test | Diebold-Mariano test for predictive accuracy
dshw | Double-Seasonal Holt-Winters Forecasting

## -- E --

easter | Easter holidays in each season
ets | Exponential smoothing state space model

## -- F --

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

## -- G --

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

## -- H --

holt | Exponential smoothing forecasts
hw | Exponential smoothing forecasts

## -- I --

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?

## -- M --

ma | Moving-average smoothing
meanf | Mean Forecast
mforecast | Forecasting time series
modelAR | Time Series Forecasts with a user-defined model
monthdays | Number of days in each season
mstl | Multiple seasonal decomposition
msts | Multi-Seasonal Time Series

## -- N --

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

## -- O --

ocsb.test | Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots

## -- P --

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)

## -- R --

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

## -- S --

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

## -- T --

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

## -- W --

window.msts | Multi-Seasonal Time Series
wineind | Australian total wine sales
woolyrnq | Quarterly production of woollen yarn in Australia

## -- misc --

_PACKAGE | forecast: Forecasting Functions for Time Series and Linear Models
`[.msts` | Multi-Seasonal Time Series