# forecast v8.11

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 | |

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

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

CV | Cross-validation statistic | |

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

BoxCox | Box Cox Transformation | |

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

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

arfima | Fit a fractionally differenced ARFIMA model | |

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

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

accuracy | Accuracy measures for a forecast model | |

dshw | Double-Seasonal Holt-Winters Forecasting | |

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

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

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

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

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

easter | Easter holidays in each season | |

gglagplot | Time series lag ggplots | |

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

forecast.ets | Forecasting using ETS models | |

ggmonthplot | Create a seasonal subseries ggplot | |

forecast.baggedModel | Forecasting using a bagged model | |

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

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

baggedModel | Forecasting using a bagged model | |

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

bizdays | Number of trading days in each season | |

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

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

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

ets | Exponential smoothing state space model | |

forecast | Forecasting time series | |

findfrequency | Find dominant frequency of a time series | |

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

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

gold | Daily morning gold prices | |

Arima | Fit ARIMA model to univariate time series | |

rwf | Naive and Random Walk Forecasts | |

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

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

ma | Moving-average smoothing | |

meanf | Mean Forecast | |

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

is.constant | Is an object constant? | |

mstl | Multiple seasonal decomposition | |

splinef | Cubic Spline Forecast | |

forecast.mts | Forecasting time series | |

msts | Multi-Seasonal Time Series | |

plot.ets | Plot components from ETS model | |

forecast.nnetar | Forecasting using neural network models | |

plot.forecast | Forecast plot | |

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

nnetar | Neural Network Time Series Forecasts | |

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

seasonal | Extract components from a time series decomposition | |

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

forecast.stl | Forecasting using stl objects | |

fourier | Fourier terms for modelling seasonality | |

tsCV | Time series cross-validation | |

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

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

ggseasonplot | Seasonal plot | |

subset.ts | Subsetting a time series | |

monthdays | Number of days in each season | |

autoplot.mforecast | Multivariate forecast plot | |

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

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

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

ses | Exponential smoothing forecasts | |

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

StatForecast | Forecast plot | |

gas | Australian monthly gas production | |

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

thetaf | Theta method forecast | |

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

reexports | Objects exported from other packages | |

wineind | Australian total wine sales | |

seasonaldummy | Seasonal dummy variables | |

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

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

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

plot.bats | Plot components from BATS model | |

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

sindexf | Forecast seasonal index | |

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

ggtsdisplay | Time series display | |

taylor | Half-hourly electricity demand | |

seasadj | Seasonal adjustment | |

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

woolyrnq | Quarterly production of woollen yarn in Australia | |

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 | 7.0.2 |

NeedsCompilation | yes |

Packaged | 2020-02-09 06:22:31 UTC; robjhyndman |

Repository | CRAN |

Date/Publication | 2020-02-09 12:20:02 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)
```