# forecast v8.12

Monthly downloads

## Forecasting Functions for Time Series and Linear Models

Methods and tools for displaying and analysing
univariate time series forecasts including exponential smoothing
via state space models and automatic ARIMA modelling.

## Readme

# 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

- Get started in forecasting with the online textbook at http://OTexts.org/fpp2/
- Read the Hyndsight blog at https://robjhyndman.com/hyndsight/
- Ask forecasting questions on http://stats.stackexchange.com/tags/forecasting
- Ask R questions on http://stackoverflow.com/tags/forecasting+r
- Join the International Institute of Forecasters: http://forecasters.org/

## License

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

## Functions in forecast

Name | Description | |

autoplot.decomposed.ts | Plot time series decomposition components using ggplot | |

arimaorder | Return the order of an ARIMA or ARFIMA model | |

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

dshw | Double-Seasonal Holt-Winters Forecasting | |

arima.errors | Errors from a regression model with ARIMA errors | |

forecast.mts | Forecasting time series | |

CV | Cross-validation statistic | |

forecast.HoltWinters | Forecasting using Holt-Winters objects | |

easter | Easter holidays in each season | |

forecast.nnetar | Forecasting using neural network models | |

fourier | Fourier terms for modelling seasonality | |

CVar | k-fold Cross-Validation applied to an autoregressive model | |

forecast.StructTS | Forecasting using Structural Time Series models | |

bld.mbb.bootstrap | Box-Cox and Loess-based decomposition bootstrap. | |

autolayer | Create a ggplot layer appropriate to a particular data type | |

mstl | Multiple seasonal decomposition | |

forecast.fracdiff | Forecasting using ARIMA or ARFIMA models | |

msts | Multi-Seasonal Time Series | |

is.constant | Is an object constant? | |

auto.arima | Fit best ARIMA model to univariate time series | |

fitted.ARFIMA | h-step in-sample forecasts for time series models. | |

forecast.stl | Forecasting using stl objects | |

meanf | Mean Forecast | |

forecast | Forecasting time series | |

BoxCox.lambda | Automatic selection of Box Cox transformation parameter | |

gold | Daily morning gold prices | |

autolayer.mts | Automatically create a ggplot for time series objects | |

ma | Moving-average smoothing | |

BoxCox | Box Cox Transformation | |

ndiffs | Number of differences required for a stationary series | |

baggedModel | Forecasting using a bagged model | |

dm.test | Diebold-Mariano test for predictive accuracy | |

autoplot.mforecast | Multivariate forecast plot | |

sindexf | Forecast seasonal index | |

Arima | Fit ARIMA model to univariate time series | |

nnetar | Neural Network Time Series Forecasts | |

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

reexports | Objects exported from other packages | |

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

simulate.ets | Simulation from a time series model | |

forecast.ets | Forecasting using ETS models | |

Acf | (Partial) Autocorrelation and Cross-Correlation Function Estimation | |

croston | Forecasts for intermittent demand using Croston's method | |

seasonal | Extract components from a time series decomposition | |

getResponse | Get response variable from time series model. | |

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

gghistogram | Histogram with optional normal and kernel density functions | |

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

seasonaldummy | Seasonal dummy variables | |

rwf | Naive and Random Walk Forecasts | |

plot.ets | Plot components from ETS model | |

plot.forecast | Forecast plot | |

ggtsdisplay | Time series display | |

tslm | Fit a linear model with time series components | |

na.interp | Interpolate missing values in a time series | |

gglagplot | Time series lag ggplots | |

tsoutliers | Identify and replace outliers in a time series | |

taylor | Half-hourly electricity demand | |

ggmonthplot | Create a seasonal subseries ggplot | |

is.acf | Is an object a particular model type? | |

bizdays | Number of trading days in each season | |

wineind | Australian total wine sales | |

is.forecast | Is an object a particular forecast type? | |

subset.ts | Subsetting a time series | |

ets | Exponential smoothing state space model | |

nsdiffs | Number of differences required for a seasonally stationary series | |

findfrequency | Find dominant frequency of a time series | |

forecast.baggedModel | Forecasting using a bagged model | |

ocsb.test | Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots | |

residuals.forecast | Residuals for various time series models | |

seasadj | Seasonal adjustment | |

forecast.bats | Forecasting using BATS and TBATS models | |

modelAR | Time Series Forecasts with a user-defined model | |

plot.Arima | Plot characteristic roots from ARIMA model | |

plot.bats | Plot components from BATS model | |

forecast.modelAR | Forecasting using user-defined model | |

gas | Australian monthly gas production | |

tsCV | Time series cross-validation | |

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

splinef | Cubic Spline Forecast | |

monthdays | Number of days in each season | |

StatForecast | Forecast plot | |

ggseasonplot | Seasonal plot | |

ses | Exponential smoothing forecasts | |

tbats.components | Extract components of a TBATS model | |

tsclean | Identify and replace outliers and missing values in a time series | |

woolyrnq | Quarterly production of woollen yarn in Australia | |

thetaf | Theta method forecast | |

autoplot.acf | ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting | |

arfima | Fit a fractionally differenced ARFIMA model | |

accuracy | Accuracy measures for a forecast model | |

No Results! |

## Vignettes of forecast

Name | ||

JSS-paper.bib | ||

JSS2008.Rmd | ||

jsslogo.jpg | ||

No Results! |

## Last month downloads

## Details

LinkingTo | Rcpp (>= 0.11.0), RcppArmadillo (>= 0.2.35) |

LazyData | yes |

ByteCompile | TRUE |

BugReports | https://github.com/robjhyndman/forecast/issues |

License | GPL-3 |

URL | http://pkg.robjhyndman.com/forecast, https://github.com/robjhyndman/forecast |

VignetteBuilder | knitr |

Encoding | UTF-8 |

RoxygenNote | 7.1.0 |

NeedsCompilation | yes |

Packaged | 2020-03-31 08:18:48 UTC; robjhyndman |

Repository | CRAN |

Date/Publication | 2020-03-31 14:10:07 UTC |

depends | , R (>= 3.0.2) |

imports | colorspace , fracdiff , ggplot2 (>= 2.2.1) , graphics , lmtest , magrittr , nnet , parallel , Rcpp (>= 0.11.0) , stats , timeDate , tseries , urca , zoo |

suggests | knitr , methods , rmarkdown , rticles , testthat , uroot |

linkingto | RcppArmadillo (>= 0.2.35) |

Contributors | Earo Wang, Leanne Chhay, Fotios Petropoulos, R Core team, Yuan Tang, Christoph Bergmeir, Slava Razbash, David Shaub, Zhenyu Zhou, Ross Ihaka, Mitchell O'Hara-Wild, George Athanasopoulos, Gabriel Caceres, Farah Yasmeen, Daniel Reid |

#### Include our badge in your README

```
[![Rdoc](http://www.rdocumentation.org/badges/version/forecast)](http://www.rdocumentation.org/packages/forecast)
```