README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r setup, include = FALSE}
knitr::opts_chunk$set(
	collapse = TRUE,
	comment = "#>",
	fig.path = "man/figures/README-",
	out.width = "100%"
)
```

# fable.binary
<!-- badges: start -->

The R package *fable.binary* provides a collection of time series forecasting models suitable for binary time series. These models work within the fable framework, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.

## Installation

You can install the **development** version from
[GitHub](https://github.com/tidyverts/fable.binary)

```{r gh-installation, eval = FALSE}
# install.packages("remotes")
remotes::install_github("tidyverts/fable.binary")
```

## Examples

```{r example, echo = TRUE}
library(fable.binary)
library(ggplot2)
library(dplyr)

# Fit models
fit <- melb_rain |>
	model(
		nn = BINNET(Wet ~ fourier(K = 1, period = "year")),
		logistic = LOGISTIC(Wet ~ fourier(K = 5, period = "year"))
	)

# Functions for computing on models
fit |> tidy()
fit |> select(logistic) |> glance()
fit |> select(logistic) |> report()
fit |> select(nn) |> glance()
fit |> select(nn) |> report()
augment(fit)

# Produce forecasts. For neural network, use
fc <- forecast(fit, h = "2 years")
as_tibble(fc) |>
	ggplot(aes(x = Date, y = .mean, col = .model)) +
	geom_line() +
	labs(y = "Probability of rain")
```