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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

184,084

Version

8.15

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Rob Hyndman

Last Published

June 1st, 2021

Functions in forecast (8.15)

arimaorder

Return the order of an ARIMA or ARFIMA model
BoxCox.lambda

Automatic selection of Box Cox transformation parameter
CV

Cross-validation statistic
Acf

(Partial) Autocorrelation and Cross-Correlation Function Estimation
CVar

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

Box Cox Transformation
arfima

Fit a fractionally differenced ARFIMA model
accuracy

Accuracy measures for a forecast model
arima.errors

Errors from a regression model with ARIMA errors
autoplot.acf

ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting
Arima

Fit ARIMA model to univariate time series
auto.arima

Fit best ARIMA model to univariate time series
autolayer.mts

Automatically create a ggplot for time series objects
baggedModel

Forecasting using a bagged model
checkresiduals

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

Create a ggplot layer appropriate to a particular data type
bld.mbb.bootstrap

Box-Cox and Loess-based decomposition bootstrap.
ets

Exponential smoothing state space model
forecast.fracdiff

Forecasting using ARIMA or ARFIMA models
autoplot.decomposed.ts

Plot time series decomposition components using ggplot
findfrequency

Find dominant frequency of a time series
dshw

Double-Seasonal Holt-Winters Forecasting
forecast.HoltWinters

Forecasting using Holt-Winters objects
forecast.baggedModel

Forecasting using a bagged model
easter

Easter holidays in each season
forecast.bats

Forecasting using BATS and TBATS models
forecast.mlm

Forecast a multiple linear model with possible time series components
fitted.ARFIMA

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

Number of trading days in each season
bats

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

Forecasting Functions for Time Series and Linear Models
forecast.modelAR

Forecasting using user-defined model
croston

Forecasts for intermittent demand using Croston's method
forecast

Forecasting time series
mstl

Multiple seasonal decomposition
msts

Multi-Seasonal Time Series
dm.test

Diebold-Mariano test for predictive accuracy
ggmonthplot

Create a seasonal subseries ggplot
is.acf

Is an object a particular model type?
gglagplot

Time series lag ggplots
is.forecast

Is an object a particular forecast type?
gold

Daily morning gold prices
is.constant

Is an object constant?
forecast.mts

Forecasting time series
forecast.nnetar

Forecasting using neural network models
ma

Moving-average smoothing
forecast.StructTS

Forecasting using Structural Time Series models
forecast.ets

Forecasting using ETS models
gas

Australian monthly gas production
forecast.lm

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

Forecasting using stl objects
StatForecast

Forecast plot
na.interp

Interpolate missing values in a time series
rwf

Naive and Random Walk Forecasts
fourier

Fourier terms for modelling seasonality
nsdiffs

Number of differences required for a seasonally stationary series
meanf

Mean Forecast
ocsb.test

Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots
tsoutliers

Identify and replace outliers in a time series
autoplot.mforecast

Multivariate forecast plot
reexports

Objects exported from other packages
seasonaldummy

Seasonal dummy variables
getResponse

Get response variable from time series model.
tbats.components

Extract components of a TBATS model
seasonal

Extract components from a time series decomposition
ggseasonplot

Seasonal plot
monthdays

Number of days in each season
ses

Exponential smoothing forecasts
modelAR

Time Series Forecasts with a user-defined model
plot.ets

Plot components from ETS model
plot.forecast

Forecast plot
thetaf

Theta method forecast
wineind

Australian total wine sales
tsclean

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

Forecast seasonal index
tsCV

Time series cross-validation
simulate.ets

Simulation from a time series model
gghistogram

Histogram with optional normal and kernel density functions
seasadj

Seasonal adjustment
plot.bats

Plot components from BATS model
residuals.forecast

Residuals for various time series models
plot.Arima

Plot characteristic roots from ARIMA model
ndiffs

Number of differences required for a stationary series
taylor

Half-hourly electricity demand
splinef

Cubic Spline Forecast
tbats

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

Neural Network Time Series Forecasts
subset.ts

Subsetting a time series
tslm

Fit a linear model with time series components
woolyrnq

Quarterly production of woollen yarn in Australia
ggtsdisplay

Time series display