Getting started with spooky 2.0

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Giancarlo Vercellino

2026-09-07

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
#> Spooky 2.0 fit
#> Features: 2 
#> Candidates: 1
fit$best_model$testing_errors
#>                   me      mae       mse rmsse      mape      rmae
#> IBM.Close  -10.25244 14.42115  324.6378   NaN 0.1115564 0.9997535
#> MSFT.Close  61.89077 61.92975 6714.8292   NaN 0.3304639 0.9997016
head(fit$best_model$preds[[1]])
#>        min      10%      25%      50%      75%      90%      max     mean
#> 1 127.2167 130.4788 135.3719 143.5271 151.6823 156.5754 159.8375 143.5271
#> 2 126.6978 130.0118 134.9827 143.2676 151.5526 156.5235 159.8375 143.2676
#> 3 126.6839 129.9993 134.9723 143.2607 151.5491 156.5221 159.8375 143.2607
#> 4 127.2456 130.5048 135.3936 143.5415 151.6895 156.5783 159.8375 143.5415
#> 5 127.2225 130.4840 135.3762 143.5300 151.6837 156.5760 159.8375 143.5300
#> 6 126.5231 129.8545 134.8517 143.1803 151.5089 156.5060 159.8375 143.1803
#>         sd
#> 1 23.06637
#> 2 23.43329
#> 3 23.44309
#> 4 23.04595
#> 5 23.06229
#> 6 23.55685

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
#>            dice       mae
#> state 0.3255814 0.5616345

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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.