---
title: "Getting started with proteus"
author: "Giancarlo Vercellino"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting started with proteus}
  %\VignetteEngine{knitr::rmarkdown}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE)
```

`proteus` fits a variational sequence-to-sequence model to one or more time
features and returns forecasts, uncertainty summaries, diagnostic plots, and
error metrics. Version 2.0 keeps the neural network and plotting dependencies
small; optional packages are loaded only when their feature is requested.

## A first forecast

The package includes `amzn_aapl_fb`, a data frame with daily prices and a date
column. A compact run is:

```{r forecast}
library(proteus)
fit <- proteus(amzn_aapl_fb, target = "AMZN", dates = "Date",
               past = 30, future = 10, epochs = 5,
               future_plan = "future::sequential", verbose = FALSE)
fit$prediction$AMZN
```

The `prediction` table contains quantiles, location and scale summaries, and
distribution diagnostics. `fit$plot$AMZN` visualizes the historical series
and forecast interval, while `fit$features_errors` reports back-test metrics.

## Optional features

Set `smoother = TRUE` or use `omit = FALSE` with missing values to opt into
`fANCOVA` or `imputeTS`, respectively. Parallel cross-validation can be enabled
with `future_plan = "future::multisession"` after installing `future` and
`furrr`. The default sequential plan works with the core dependencies.