murphet 1.3.0

Bayesian time‑series model with Beta/Gaussian heads, smooth changepoints and seasonality

📈 Murphet — Prophet's (0-1) Cousin for Probabilities & Rates

A Stan-powered time-series model that never breaks the 0 - 1 bounds and still feels like Prophet.

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1 · Why Murphet?

Problem with vanilla Prophet	How Murphet fixes it
Forecasts of rates can shoot < 0 or > 1	Beta head maps μ → (0, 1) automatically
Constant-variance Gaussian noise mis-prices tails	Mean-dependent Beta / Student-t likelihoods
Hard CPs create kinks; few data → over-fit	Smooth logistic changepoints + Laplace shrinkage
Season coefficients often blow up	Weak-Normal (σ≈10) priors, optional horseshoe
Residual AR left untreated	Latent AR(1) disturbance (ρ, μ₀)
One-size-fits-all variance	Heteroscedastic φᵢ/σᵢ via log-linear link

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2 · Installation

pip install murphet              # wheels include pre-compiled Stan models

Requirements

• Python >= 3.8 & CmdStanPy >= 1.0 (auto-installed)
• A recent CmdStan toolchain (gcc/clang) — cmdstanpy.install_cmdstan() will fetch it.

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3 · Quick start

import pandas as pd, numpy as np
from murphet import fit_churn_model

df = pd.read_csv("churn_data.csv")        # cols: ds, y  (0<y<1)
df["ds"] = pd.to_datetime(df["ds"])
df["t"]  = np.arange(len(df))             # integer index

mod = fit_churn_model(
        t              = df["t"],
        y              = df["y"],
        periods        = 12, num_harmonics = 3,    # yearly seasonality
        n_changepoints = 4,
        likelihood     = "beta",                   # default & safest
        inference      = "nuts",                   # full posterior
      )

future_t = np.arange(len(df), len(df)+6)
fcst     = mod.predict(future_t)

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4 · Model internals (nutshell)

Component	Equation	Note
Trend	μdet(t) = k·t + m + ∑ δj σ(γ (t − sj))	smooth CP ramps
Seasonality	Fourier blocks on raw t (fmod)	multiple periods OK
Link / saturation	μ → logit⁻¹ → p	optional
Likelihoods	Beta(p·φᵢ,(1-p)·φᵢ) or Student-tν(μ,σᵢ)	φᵢ / σᵢ heteroscedastic
Latent error	y* = μdet + ρ·lag	AR(1) disturbance

Add-ons implemented

✔ add-on	Stan code snippet	Effect
AR(1) latent error	real<lower=-1,upper=1> rho; real mu0; + update in partial_sum_*	absorbs slow drifts / residual autocorr
Heteroscedastic precision / scale	phi_i = exp(log_phi0 - beta_phi*abs(mu_det)); (Beta) / sigma_i = exp(log_sigma0 + beta_sigma*abs(mu_det)); (Gauss)	wider tails when level high
Heavy-tail option	`student_t_lpdf(y	ν, μ, σᵢ)withν ~ Exp(1/30)`

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5 · Case-studies

5 a · Hong-Kong hotel occupancy (monthly, 2020-2025)

Source link

Hold-out horizon	RMSE
Murphet β	0.0916
Prophet (optimised)	0.1159

5 b · U.S. Retail Inventories-to-Sales Ratio (FRED RETAILIRNSA)

Hold-out (24 mo)	RMSE	SMAPE
Murphet β	0.0496	5.15 %
Prophet	0.1140	13.21 %

Residual check:

Murphet's AR(1)+heteroscedastic head slashes autocorrelation; Prophet still shows structure.

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6 · Which head to choose?

Head	Use-case	Link	Likelihood
β (default)	Proportions, CTR, churn %, conversion %	logit⁻¹	Beta(p·φᵢ,(1-p)·φᵢ)
Gaussian / Student-t	Ratios "around" 0.4–1.0 or unbounded KPI	identity	Normal/Student-t(μ,σᵢ)

Switch with likelihood="gaussian"; all other API calls identical.

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7 · API cheat-sheet

Function	Purpose
fit_churn_model(t, y, **kwargs)	fit (MAP, ADVI, or NUTS)
model.predict(t_new)	fast vectorised prediction
model.fit_result	access raw CmdStanPy object
model.summary()	pretty DataFrame of posteriors

Key kwargs:

periods, num_harmonics         # seasonality
n_changepoints, delta_scale    # trend flexibility
gamma_scale                    # CP steepness
season_scale                   # weaken/strengthen Fourier priors
likelihood  = "beta"|"gaussian"
inference   = "map"|"advi"|"nuts"

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8 · Road-map

• Holiday regressors (Prophet style)
• Prophet-like plotting helpers
• Automatic Stan/C++ speed-ups for long MCMC chains

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9 · Citing Murphet

If you use Murphet in academic work, please cite:

Murphy, S. (2025). Murphet: A Bayesian Time-Series Model for Bounded Rates.
https://github.com/halsted312/murphet

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