Function reference • forecast

Package Forecast package
`forecast-package`	Forecasting Functions for Time Series and Linear Models
Time series analysis Functions for working with time series
`msts()`	Multi-Seasonal Time Series
`subset(<ts>)` `subset(<msts>)`	Subsetting a time series
`bizdays()`	Number of trading days in each season
`easter()`	Easter holidays in each season
`monthdays()`	Number of days in each season
`fourier()` `fourierf()`	Fourier terms for modelling seasonality
`seasonaldummy()` `seasonaldummyf()`	Seasonal dummy variables
`findfrequency()`	Find dominant frequency of a time series
`BoxCox()` `InvBoxCox()`	Box Cox Transformation
`BoxCox.lambda()`	Automatic selection of Box Cox transformation parameter
`tsclean()`	Identify and replace outliers and missing values in a time series
`tsoutliers()`	Identify and replace outliers in a time series
`na.interp()`	Interpolate missing values in a time series
`ndiffs()`	Number of differences required for a stationary series
`nsdiffs()`	Number of differences required for a seasonally stationary series
`bld.mbb.bootstrap()`	Box-Cox and Loess-based decomposition bootstrap.
`is.constant()`	Is an object constant?
Seasonal decomposition Functions used in seasonal decomposition
`ma()`	Moving-average smoothing
`mstl()`	Multiple seasonal decomposition
`seasadj()`	Seasonal adjustment
`seasonal()` `trendcycle()` `remainder()`	Extract components from a time series decomposition
Modelling Functions for estimating time series models
`arfima()`	Fit a fractionally differenced ARFIMA model
`Arima()`	Fit ARIMA model to univariate time series
`auto.arima()`	Fit best ARIMA model to univariate time series
`ets()`	Exponential smoothing state space model
`baggedModel()` `baggedETS()`	Forecasting using a bagged model
`bats()`	BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
`tbats()`	TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
`nnetar()`	Neural Network Time Series Forecasts
`forecast(<stl>)` `stlm()` `forecast(<stlm>)` `stlf()`	Forecasting using stl objects
`tslm()`	Fit a linear model with time series components
Forecasting Functions for producing forecasts
`rwf()` `naive()` `snaive()`	Naive and Random Walk Forecasts
`meanf()`	Mean Forecast
`ses()` `holt()` `hw()`	Exponential smoothing forecasts
`dshw()`	Double-Seasonal Holt-Winters Forecasting
`forecast(<stl>)` `stlm()` `forecast(<stlm>)` `stlf()`	Forecasting using stl objects
`splinef()`	Cubic Spline Forecast
`thetaf()`	Theta method forecast
`croston()`	Forecasts for intermittent demand using Croston's method
`sindexf()`	Forecast seasonal index
`forecast()`	Forecasting time series
`forecast(<fracdiff>)` `forecast(<Arima>)` `forecast(<ar>)`	Forecasting using ARIMA or ARFIMA models
`forecast(<ets>)`	Forecasting using ETS models
`forecast(<baggedModel>)`	Forecasting using a bagged model
`forecast(<bats>)` `forecast(<tbats>)`	Forecasting using BATS and TBATS 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(<mts>)`	Forecasting time series
`forecast(<nnetar>)`	Forecasting using neural network models
`forecast(<StructTS>)`	Forecasting using Structural Time Series models
Plotting Functions for plotting time series and forecasts
`gghistogram()`	Histogram with optional normal and kernel density functions
`ggseasonplot()` `seasonplot()`	Seasonal plot
`ggmonthplot()` `ggsubseriesplot()`	Create a seasonal subseries ggplot
`gglagplot()` `gglagchull()`	Time series lag ggplots
`Acf()` `Pacf()` `Ccf()` `taperedacf()` `taperedpacf()`	(Partial) Autocorrelation and Cross-Correlation Function Estimation
`autoplot(<acf>)` `ggAcf()` `ggPacf()` `ggCcf()` `autoplot(<mpacf>)` `ggtaperedacf()` `ggtaperedpacf()`	ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting
`ggtsdisplay()` `tsdisplay()`	Time series display
`checkresiduals()`	Check that residuals from a time series model look like white noise
`plot(<Arima>)` `plot(<ar>)` `autoplot(<Arima>)` `autoplot(<ar>)`	Plot characteristic roots from ARIMA model
`plot(<bats>)` `autoplot(<tbats>)` `autoplot(<bats>)` `plot(<tbats>)`	Plot components from BATS model
`plot(<ets>)` `autoplot(<ets>)`	Plot components from ETS model
`plot(<forecast>)` `autoplot(<forecast>)` `autoplot(<splineforecast>)` `autolayer(<forecast>)` `plot(<splineforecast>)`	Forecast plot
`autoplot(<mforecast>)` `autolayer(<mforecast>)` `plot(<mforecast>)`	Multivariate forecast plot
`autoplot(<decomposed.ts>)` `autoplot(<stl>)` `autoplot(<StructTS>)` `autoplot(<seas>)` `autoplot(<mstl>)`	Plot time series decomposition components using ggplot
`autolayer(<mts>)` `autolayer(<msts>)` `autolayer(<ts>)` `autoplot(<ts>)` `autoplot(<mts>)` `autoplot(<msts>)` `fortify(<ts>)`	Automatically create a ggplot for time series objects
`autolayer()`	Create a ggplot layer appropriate to a particular data type
Model analysis Functions for analysing time series models
`fitted(<fracdiff>)` `fitted(<Arima>)` `fitted(<ar>)` `fitted(<bats>)` `fitted(<ets>)` `fitted(<modelAR>)` `fitted(<nnetar>)` `fitted(<tbats>)`	h-step in-sample forecasts for time series models.
`residuals(<forecast>)` `residuals(<ar>)` `residuals(<Arima>)` `residuals(<bats>)` `residuals(<tbats>)` `residuals(<ets>)` `residuals(<fracdiff>)` `residuals(<nnetar>)` `residuals(<stlm>)` `residuals(<tslm>)`	Residuals for various time series models
`checkresiduals()`	Check that residuals from a time series model look like white noise
`arimaorder()`	Return the order of an ARIMA or ARFIMA model
`simulate(<ets>)` `simulate(<Arima>)` `simulate(<ar>)` `simulate(<lagwalk>)` `simulate(<fracdiff>)` `simulate(<nnetar>)` `simulate(<modelAR>)`	Simulation from a time series model
`tbats.components()`	Extract components of a TBATS model
Forecast evaluation Functions used for evaluating forecasts
`accuracy()`	Accuracy measures for a forecast model
`CVar()`	k-fold Cross-Validation applied to an autoregressive model
`CV()`	Cross-validation statistic
`tsCV()`	Time series cross-validation
`dm.test()`	Diebold-Mariano test for predictive accuracy
Data Data sets included in the package
`gas`	Australian monthly gas production
`gold`	Daily morning gold prices
`taylor`	Half-hourly electricity demand
`wineind`	Australian total wine sales
`woolyrnq`	Quarterly production of woollen yarn in Australia

Reference

Package

Time series analysis

Seasonal decomposition

Modelling

Forecasting

Plotting

Model analysis

Forecast evaluation

Data

Contents