---
title: "Getting started with spooky 2.0"
author: "Giancarlo Vercellino"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting started with spooky 2.0}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

## Overview

`spooky` forecasts one or more time features with a compact spectral model. It
uses differencing, FFT extrapolation, rolling validation, and jackknife-style
resampling to compare candidate sequence lengths and leave-out values.

Spooky 2.0 has no runtime dependencies beyond base R packages.

## Numeric forecasting

The package includes `time_features`, a small example data set with two numeric
series. The following fits one candidate model and keeps the example fast.

```{r numeric}
data(time_features)
fit <- spooky(time_features, seq_len = 10, lno = 1,
              n_samp = 1, n_windows = 2, seed = 42)
fit
fit$best_model$testing_errors
head(fit$best_model$preds[[1]])
```

The `history` component records the candidate settings and validation errors.
The `best_model` component contains errors, prediction summaries, and plot
objects for each input feature.

## Categorical forecasting

Categorical columns are encoded internally, so no dummy-variable package is
needed.

```{r categorical}
events <- data.frame(state = factor(rep(c("quiet", "active"), 30)))
categorical_fit <- spooky(events, seq_len = 2, lno = 1,
                          n_samp = 1, n_windows = 2, seed = 42)
categorical_fit$best_model$testing_errors
```

## Reproducibility

Set `seed` to make the random candidate search reproducible. For a larger
search, provide ranges for `seq_len` and `lno`, and increase `n_samp`.

```{r search, eval=FALSE}
fit <- spooky(time_features,
              seq_len = c(5, 30), lno = c(1, 10),
              n_samp = 30, n_windows = 3, seed = 42)
```