tsseg.algorithms.prophet package

Prophet — trend change point detection via Facebook Prophet.

Description

This detector wraps Facebook Prophet’s piecewise-linear trend model. Prophet specifies a large number of potential change points uniformly placed in the first 80 % of the time series, then applies an L1-regularised (sparse) prior on the rate-change magnitudes so that most potential points go unused. The detector extracts the locations of the significant rate changes from the fitted model.

Key parameters:

n_changepoints — number of potential change points.
changepoint_prior_scale (tunable inside cost_params) — controls trend flexibility (default 0.05; increase for a more flexible trend).
changepoint_range (tunable inside cost_params) — fraction of the history where change points are allowed (default 0.8).

Type: change point detection
Supervision: semi-supervised (n_changepoints recommended)
Scope: univariate (multivariate via ensembling)
Requires: prophet and cmdstanpy

Parameters

Name	Type	Default	Description
`n_changepoints`	int / None	`5`	Number of potential change points.
`n_changepoint_func`	callable / None	`None`	Callable that determines `n_changepoints` from the series.
`multivariate_strategy`	str	`"ensembling"`	Strategy for multivariate series (`"ensembling"` or `"l2"`).
`tolerance`	float	`0.01`	Tolerance for change-point deduplication.
`axis`	int	`0`	Time axis.

Usage

from tsseg.algorithms import ProphetDetector

detector = ProphetDetector(n_changepoints=10)
labels = detector.fit_predict(X)

Implementation: Wrapper around facebook/prophet. MIT.

Reference: Taylor & Letham (2018), Forecasting at Scale, The American Statistician.

tsseg.algorithms.prophet package

Description

Parameters

Usage

Submodules

tsseg.algorithms.prophet.detector module

Module contents