Package index • forecast

Package

Forecast package

forecast-package: forecast: Forecasting Functions for Time Series and Linear Models

Time series analysis

Functions for working with time series

bizdays(): Number of trading days in each season

bld.mbb.bootstrap(): Box-Cox and Loess-based decomposition bootstrap.

BoxCox() InvBoxCox(): Box Cox Transformation

BoxCox.lambda(): Automatic selection of Box Cox transformation parameter

easter(): Easter holidays in each season

findfrequency(): Find dominant frequency of a time series

fourier() fourierf(): Fourier terms for modelling seasonality

is.constant(): Is an object constant?

monthdays(): Number of days in each season

msts(): Multi-Seasonal 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

ocsb.test(): Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots

seasonaldummy() seasonaldummyf(): Seasonal dummy variables

subset(<ts>) subset(<msts>): Subsetting a time series

tsclean(): Identify and replace outliers and missing values in a time series

tsoutliers(): Identify and replace outliers in a time series

Seasonal decomposition

Functions used in seasonal decomposition

ma(): Moving-average smoothing

mstl(): Multiple seasonal decomposition

seasonal() trendcycle() remainder(): Extract components from a time series decomposition

seasadj(): Seasonal adjustment

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

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)

croston_model(): Croston forecast model

ets(): Exponential smoothing state space model

mean_model(): Mean Forecast Model

nnetar(): Neural Network Time Series Forecasts

rw_model(): Random walk model

forecast(<stl>) stlm() forecast(<stlm>) stlf(): Forecasting using stl objects

tbats(): TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)

tslm(): Fit a linear model with time series components

Forecasting

Functions for producing forecasts

forecast(<croston_model>) croston(): Forecasts for intermittent demand using Croston's method

dshw(): Double-Seasonal Holt-Winters Forecasting

ses() holt() hw(): Exponential smoothing forecasts

forecast(<mean_model>) meanf(): Mean Forecast

modelAR(): Time Series Forecasts with a user-defined model

forecast(<rw_model>) rwf() naive() snaive(): Naive and Random Walk Forecasts

sindexf(): Forecast seasonal index

splinef(): Cubic Spline Forecast

forecast(<stl>) stlm() forecast(<stlm>) stlf(): Forecasting using stl objects

thetaf(): Theta method forecast

forecast(<fracdiff>) forecast(<Arima>) forecast(<ar>): Forecasting using ARIMA or ARFIMA models

forecast(<baggedModel>): Forecasting using a bagged model

forecast(<bats>) forecast(<tbats>): Forecasting using BATS and TBATS models

forecast(<ets>): Forecasting using ETS 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(<StructTS>): Forecasting using Structural Time Series models

forecast(<ts>) forecast(<default>) print(<forecast>): Forecasting time series

is.forecast() is.mforecast() is.splineforecast(): Is an object a particular forecast type?

Plotting

Functions for plotting time series and forecasts

Acf() Pacf() Ccf() taperedacf() taperedpacf(): (Partial) Autocorrelation and Cross-Correlation Function Estimation

checkresiduals(): Check that residuals from a time series model look like white noise

autoplot(<acf>) ggAcf() ggPacf() ggCcf() autoplot(<mpacf>) ggtaperedacf() ggtaperedpacf(): ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting

gghistogram(): Histogram with optional normal and kernel density functions

gglagplot() gglagchull(): Time series lag ggplots

ggseasonplot() seasonplot(): Seasonal plot

ggmonthplot() ggsubseriesplot(): Create a seasonal subseries ggplot

is.acf() is.Arima() is.baggedModel() is.bats() is.ets() is.modelAR() is.stlm() is.nnetar() is.nnetarmodels(): Is an object a particular model type?

autolayer(): Create a ggplot layer appropriate to a particular data type

autolayer(<mts>) autolayer(<msts>) autolayer(<ts>) autoplot(<ts>) autoplot(<mts>) autoplot(<msts>) fortify(<ts>): Automatically create a ggplot for time series objects

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

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

ggtsdisplay() tsdisplay(): Time series display

StatForecast GeomForecast geom_forecast(): Forecast plot

Model analysis

Functions for analysing time series models

arima.errors(): Errors from a regression model with ARIMA errors

arimaorder(): Return the order of an ARIMA or ARFIMA model

checkresiduals(): Check that residuals from a time series model look like white noise

getResponse(): Get response variable from time series model.

modeldf(): Compute model degrees of freedom

fitted(<ARFIMA>) 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(<ARFIMA>) residuals(<nnetar>) residuals(<stlm>) residuals(<tslm>): Residuals for various time series models

simulate(<ets>) simulate(<Arima>) simulate(<ar>) simulate(<rw_model>) simulate(<fracdiff>) simulate(<nnetar>) simulate(<modelAR>) simulate(<tbats>): Simulation from a time series model

tbats.components(): Extract components of a TBATS model

Forecast evaluation

Functions used for evaluating forecasts

accuracy(<default>): Accuracy measures for a forecast model

CV(): Cross-validation statistic

CVar(): k-fold Cross-Validation applied to an autoregressive model

dm.test(): Diebold-Mariano test for predictive accuracy

tsCV(): Time series cross-validation

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