# forecast v8.10

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 | |

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

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

dshw | Double-Seasonal Holt-Winters Forecasting | |

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

easter | Easter holidays in each season | |

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

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

CV | Cross-validation statistic | |

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

baggedModel | Forecasting using a bagged model | |

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

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

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

bizdays | Number of trading days in each season | |

forecast.ets | Forecasting using ETS models | |

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

findfrequency | Find dominant frequency of a time series | |

ets | Exponential smoothing state space model | |

forecast | Forecasting time series | |

forecast.baggedModel | Forecasting using a bagged model | |

gglagplot | Time series lag ggplots | |

ma | Moving-average smoothing | |

ggmonthplot | Create a seasonal subseries ggplot | |

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

meanf | Mean Forecast | |

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

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

gas | Australian monthly gas production | |

StatForecast | Forecast plot | |

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

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

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

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

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

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

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

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

ses | Exponential smoothing forecasts | |

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

ggseasonplot | Seasonal plot | |

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

nnetar | Neural Network Time Series Forecasts | |

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

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

seasadj | Seasonal adjustment | |

monthdays | Number of days in each season | |

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

forecast.stl | Forecasting using stl objects | |

fourier | Fourier terms for modelling seasonality | |

mstl | Multiple seasonal decomposition | |

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

forecast.nnetar | Forecasting using neural network models | |

is.constant | Is an object constant? | |

gold | Daily morning gold prices | |

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

forecast.mts | Forecasting time series | |

msts | Multi-Seasonal Time Series | |

autoplot.mforecast | Multivariate forecast plot | |

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

taylor | Half-hourly electricity demand | |

woolyrnq | Quarterly production of woollen yarn in Australia | |

plot.bats | Plot components from BATS model | |

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

thetaf | Theta method forecast | |

sindexf | Forecast seasonal index | |

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

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

reexports | Objects exported from other packages | |

splinef | Cubic Spline Forecast | |

ggtsdisplay | Time series display | |

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

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

subset.ts | Subsetting a time series | |

wineind | Australian total wine sales | |

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

plot.forecast | Forecast plot | |

seasonal | Extract components from a time series decomposition | |

plot.ets | Plot components from ETS model | |

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

rwf | Naive and Random Walk Forecasts | |

seasonaldummy | Seasonal dummy variables | |

tsCV | Time series cross-validation | |

BoxCox | Box Cox Transformation | |

accuracy | Accuracy measures for a forecast model | |

Arima | Fit ARIMA model to univariate time series | |

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

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

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

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

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

arfima | Fit a fractionally differenced ARFIMA 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 | 7.0.1.9000 |

NeedsCompilation | yes |

Packaged | 2019-12-03 22:35:15 UTC; robjhyndman |

Repository | CRAN |

Date/Publication | 2019-12-05 12:10:02 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)
```