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naive v2: dependency-free empirical extrapolation

naive searches historical windows for recurring patterns and uses similar windows to form an empirical forecast distribution. The runtime package uses only base R.

set.seed(1)
x <- data.frame(signal = sin(seq(0, 12, length.out = 120)) + rnorm(120, 0, .05))
fit <- naive_fit(x, seq_len = 8, n_windows = 3, n_samp = 4, seed = 42)
print(fit)
#> naive empirical forecast
#> horizon: 8 
#>   seq_len     cover stride    method location     score
#> 1       8 0.8318448      2 euclidean     mean 0.9311625
#> 2       8 0.8496603      4 minkowski   median 0.9253822
#> 3       8 0.3289116      2 minkowski   median 0.9311625
#> 4       8 0.7643581      2 euclidean   median 0.9311625
plot(fit)

The same interface accepts categorical sequences.

events <- data.frame(state = factor(rep(c("low", "high", "medium"), 30)))
naive_forecast(events, horizon = 4, seed = 42)$forecast$state
#>   mode
#> 1 high
#> 2 high
#> 3 high
#> 4 high

Use naive_metrics() to compare a forecast against a holdout and compare the result with a last-value baseline before deploying it.

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.