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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.

This package is now retired in favour of the fable package. The forecast package will remain in its current state, and maintained with bug fixes only. For the latest features and development, we recommend forecasting with the fable package.

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

191,858

Version

8.18

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Rob Hyndman

Last Published

October 2nd, 2022

Functions in forecast (8.18)

BoxCox.lambda

Automatic selection of Box Cox transformation parameter
Arima

Fit ARIMA model to univariate time series
accuracy.default

Accuracy measures for a forecast model
BoxCox

Box Cox Transformation
arimaorder

Return the order of an ARIMA or ARFIMA model
CVar

k-fold Cross-Validation applied to an autoregressive model
autoplot.acf

ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting
autoplot.decomposed.ts

Plot time series decomposition components using ggplot
bld.mbb.bootstrap

Box-Cox and Loess-based decomposition bootstrap.
checkresiduals

Check that residuals from a time series model look like white noise
autolayer.mts

Automatically create a ggplot for time series objects
autolayer

Create a ggplot layer appropriate to a particular data type
bats

BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
auto.arima

Fit best ARIMA model to univariate time series
bizdays

Number of trading days in each season
baggedModel

Forecasting using a bagged model
dm.test

Diebold-Mariano test for predictive accuracy
forecast.HoltWinters

Forecasting using Holt-Winters objects
fitted.ARFIMA

h-step in-sample forecasts for time series models.
croston

Forecasts for intermittent demand using Croston's method
ets

Exponential smoothing state space model
forecast-package

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

Forecasting using ARIMA or ARFIMA models
findfrequency

Find dominant frequency of a time series
easter

Easter holidays in each season
dshw

Double-Seasonal Holt-Winters Forecasting
forecast.mlm

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

Forecasting time series
forecast.bats

Forecasting using BATS and TBATS models
forecast.stl

Forecasting using stl objects
forecast.ets

Forecasting using ETS models
forecast.lm

Forecast a linear model with possible time series components
forecast.nnetar

Forecasting using neural network models
forecast.modelAR

Forecasting using user-defined model
gglagplot

Time series lag ggplots
gold

Daily morning gold prices
forecast.StructTS

Forecasting using Structural Time Series models
forecast.ts

Forecasting time series
forecast.baggedModel

Forecasting using a bagged model
ggmonthplot

Create a seasonal subseries ggplot
fourier

Fourier terms for modelling seasonality
getResponse

Get response variable from time series model.
gghistogram

Histogram with optional normal and kernel density functions
mstl

Multiple seasonal decomposition
msts

Multi-Seasonal Time Series
ma

Moving-average smoothing
meanf

Mean Forecast
StatForecast

Forecast plot
modelAR

Time Series Forecasts with a user-defined model
monthdays

Number of days in each season
na.interp

Interpolate missing values in a time series
gas

Australian monthly gas production
is.constant

Is an object constant?
nsdiffs

Number of differences required for a seasonally stationary series
rwf

Naive and Random Walk Forecasts
is.acf

Is an object a particular model type?
plot.forecast

Forecast plot
plot.ets

Plot components from ETS model
ocsb.test

Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots
is.forecast

Is an object a particular forecast type?
seasadj

Seasonal adjustment
simulate.ets

Simulation from a time series model
nnetar

Neural Network Time Series Forecasts
sindexf

Forecast seasonal index
residuals.forecast

Residuals for various time series models
ndiffs

Number of differences required for a stationary series
plot.Arima

Plot characteristic roots from ARIMA model
reexports

Objects exported from other packages
autoplot.mforecast

Multivariate forecast plot
thetaf

Theta method forecast
ggseasonplot

Seasonal plot
ses

Exponential smoothing forecasts
tslm

Fit a linear model with time series components
ggtsdisplay

Time series display
tbats.components

Extract components of a TBATS model
splinef

Cubic Spline Forecast
tsCV

Time series cross-validation
subset.ts

Subsetting a time series
plot.bats

Plot components from BATS model
tbats

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

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

Quarterly production of woollen yarn in Australia
seasonal

Extract components from a time series decomposition
seasonaldummy

Seasonal dummy variables
tsoutliers

Identify and replace outliers in a time series
taylor

Half-hourly electricity demand
wineind

Australian total wine sales
arfima

Fit a fractionally differenced ARFIMA model
arima.errors

Errors from a regression model with ARIMA errors
CV

Cross-validation statistic
Acf

(Partial) Autocorrelation and Cross-Correlation Function Estimation