prophet-laplace 0.0.1

Prophet in a laplace sandwich: same API, calibrated densities

prophet-laplace

"He's not the messiah. He's a very naughty boy." (why) This package makes him useful anyway.

Prophet in a laplace sandwich: same API, calibrated densities.

from prophet_laplace import SandwichedProphet

m = SandwichedProphet(k=30)          # accepts Prophet's kwargs too
m.fit(df)                            # ds, y (+ regressors as usual)
fc = m.predict(future)               # yhat / yhat_lower / yhat_upper,
                                     # mapped back exactly

What it does

SandwichedProphet fits Prophet in the z-coordinates of a skaters laplace forecaster (the Rosenblatt transform of each observation under the predictive issued for it) and maps every forecast back through the exact inverse. Prophet keeps its calendar decomposition, holidays, and extra regressors; the sandwich supplies the volatility clock, repeated-value handling, and tails whose stated probabilities come true.

Why

Measured on 921 non-price FRED series under a pre-registered protocol (statements, frozen universe, and results in the skaters repository):

	median one-step LL vs laplace	family-weighted (120 families)
Prophet raw	-0.755 nats	-4.60 nats
Prophet sandwiched	-0.020 nats	-0.025 nats

The sandwich closes 97% of Prophet's density gap without retraining anything. predictive(step, z_mu, z_sigma) exposes the full y-space density (logpdf, cdf, quantile) for scoring and risk use.

The same construction lifts other forecasters and detectors too: skaters.microprediction.org/sandwich.html.

Status

v0: future frames only, horizons past k reuse the k-step transport (disclosed approximation). The goal is to demonstrate interoperability, then propose the option upstream.