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forecast

The R package forecast provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

A complementary forecasting package is the fable package, which implements many of the same models but in a tidyverse framework.

Installation

You can install the stable version from CRAN.

install.packages("forecast", dependencies = TRUE)

You can install the development version from Github

# install.packages("remotes")
remotes::install_github("robjhyndman/forecast")

Usage

library(forecast)
library(ggplot2)

# ETS forecasts
USAccDeaths |>
  ets() |>
  forecast() |>
  autoplot()

# Automatic ARIMA forecasts
WWWusage |>
  auto.arima() |>
  forecast(h=20) |>
  autoplot()

# ARFIMA forecasts
library(fracdiff)
x <- fracdiff.sim( 100, ma=-.4, d=.3)$series
arfima(x) |>
  forecast(h=30) |>
  autoplot()

# Forecasting with STL
USAccDeaths |>
  stlm(modelfunction=ar) |>
  forecast(h=36) |>
  autoplot()

AirPassengers |>
  stlf(lambda=0) |>
  autoplot()

USAccDeaths |>
  stl(s.window='periodic') |>
  forecast() |>
  autoplot()

# TBATS forecasts
USAccDeaths |>
  tbats() |>
  forecast() |>
  autoplot()

taylor |>
  tbats() |>
  forecast() |>
  autoplot()

For more information

License

This package is free and open source software, licensed under GPL-3.

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Version

Install

install.packages('forecast')

Monthly Downloads

176,217

Version

8.24.0

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Rob Hyndman

Last Published

April 8th, 2025

Functions in forecast (8.24.0)

autoplot.decomposed.ts

Plot time series decomposition components using ggplot
autolayer

Create a ggplot layer appropriate to a particular data type
baggedModel

Forecasting using a bagged model
checkresiduals

Check that residuals from a time series model look like white noise
croston

Forecasts for intermittent demand using Croston's method
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.
dm.test

Diebold-Mariano test for predictive accuracy
easter

Easter holidays in each season
dshw

Double-Seasonal Holt-Winters Forecasting
ets

Exponential smoothing state space model
findfrequency

Find dominant frequency of a time series
forecast.fracdiff

Forecasting using ARIMA or ARFIMA models
forecast.HoltWinters

Forecasting using Holt-Winters objects
forecast.StructTS

Forecasting using Structural Time Series models
fitted.ARFIMA

h-step in-sample forecasts for time series models.
forecast.stl

Forecasting using stl objects
forecast-package

forecast: Forecasting Functions for Time Series and Linear Models
forecast.baggedModel

Forecasting using a bagged model
forecast.nnetar

Forecasting using neural network models
forecast.mts

Forecasting time series
forecast.modelAR

Forecasting using user-defined model
gas

Australian monthly gas production
StatForecast

Forecast plot
forecast.ts

Forecasting time series
fourier

Fourier terms for modelling seasonality
forecast.mlm

Forecast a multiple linear model with possible time series components
forecast.lm

Forecast a linear model with possible time series components
getResponse

Get response variable from time series model.
monthdays

Number of days in each season
modeldf

Compute model degrees of freedom
gghistogram

Histogram with optional normal and kernel density functions
forecast.bats

Forecasting using BATS and TBATS models
modelAR

Time Series Forecasts with a user-defined model
ggmonthplot

Create a seasonal subseries ggplot
forecast.ets

Forecasting using ETS models
gglagplot

Time series lag ggplots
is.constant

Is an object constant?
gold

Daily morning gold prices
ma

Moving-average smoothing
meanf

Mean Forecast
nnetar

Neural Network Time Series Forecasts
na.interp

Interpolate missing values in a time series
rwf

Naive and Random Walk Forecasts
msts

Multi-Seasonal Time Series
ndiffs

Number of differences required for a stationary series
is.forecast

Is an object a particular forecast type?
is.acf

Is an object a particular model type?
ocsb.test

Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots
plot.Arima

Plot characteristic roots from ARIMA model
mstl

Multiple seasonal decomposition
plot.bats

Plot components from BATS model
nsdiffs

Number of differences required for a seasonally stationary series
plot.ets

Plot components from ETS model
seasonaldummy

Seasonal dummy variables
ggseasonplot

Seasonal plot
seasadj

Seasonal adjustment
seasonal

Extract components from a time series decomposition
plot.forecast

Forecast plot
autoplot.mforecast

Multivariate forecast plot
residuals.forecast

Residuals for various time series models
reexports

Objects exported from other packages
thetaf

Theta method forecast
tsCV

Time series cross-validation
simulate.ets

Simulation from a time series model
ses

Exponential smoothing forecasts
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
sindexf

Forecast seasonal index
tsclean

Identify and replace outliers and missing values in a time series
tsoutliers

Identify and replace outliers in a time series
splinef

Cubic Spline Forecast
tbats.components

Extract components of a TBATS model
subset.ts

Subsetting a time series
ggtsdisplay

Time series display
taylor

Half-hourly electricity demand
woolyrnq

Quarterly production of woollen yarn in Australia
wineind

Australian total wine sales
arima.errors

Errors from a regression model with ARIMA errors
autoplot.acf

ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting
autolayer.mts

Automatically create a ggplot for time series objects
BoxCox.lambda

Automatic selection of Box Cox transformation parameter
CVar

k-fold Cross-Validation applied to an autoregressive model
CV

Cross-validation statistic
Arima

Fit ARIMA model to univariate time series
Acf

(Partial) Autocorrelation and Cross-Correlation Function Estimation
accuracy.default

Accuracy measures for a forecast model
BoxCox

Box Cox Transformation
arimaorder

Return the order of an ARIMA or ARFIMA model
auto.arima

Fit best ARIMA model to univariate time series
arfima

Fit a fractionally differenced ARFIMA model