forecast 
======================

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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.

A complementary forecasting package is the [fable](http://fable.tidyverts.org/) package, which implements many of the same models but in a tidyverse framework.

## Installation

You can install the **stable** version from
[CRAN](https://cran.r-project.org/package=forecast).

```r
install.packages("forecast", dependencies = TRUE)
```

You can install the **development** version from
[Github](https://github.com/robjhyndman/forecast)

```r
# install.packages("pak")
pak::pak("robjhyndman/forecast")
```

## Usage

```r
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 = -0.4, d = 0.3)$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.
