Getting started with spooky 2.0

In spooky: Time Feature Extrapolation Using Spectral Analysis and Jack-Knife Resampling

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.

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.

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.

fit <- spooky(time_features,
              seq_len = c(5, 30), lno = c(1, 10),
              n_samp = 30, n_windows = 3, seed = 42)

spooky documentation built on Sept. 7, 2026, 9:07 a.m.