tsseg 0.1.0

A library for time series segmentation

tsseg - Time Series Segmentation

tsseg is a Python library for Time Series Segmentation, covering both Change Point Detection and State Detection. It bundles 30+ segmentation algorithms, evaluation metrics, data loaders, and real-world benchmark datasets under a unified API.

Segmentation algorithms share the same base class interface as the aeon time series toolkit, making them interoperable with the broader aeon ecosystem. Several aeon segmenters are also re-exposed through tsseg for convenience.

Quick Start

from tsseg.data.datasets import load_mocap
from tsseg.algorithms import ClapDetector
from tsseg.metrics import StateMatchingScore

# Load a time series
X, y_true = load_mocap(trial=0)

# Segment
segmenter = ClapDetector()
segmenter.fit(X, axis=0)
y_pred = segmenter.predict(X)

# Evaluate
score = StateMatchingScore().compute(y_true, y_pred)
print(f"SMS: {score['score']:.4f}")

📖 Documentation: fchavelli.github.io/tsseg

Installation

Requires: conda in your PATH (or set it in a .env file — see .env.example).

git clone https://github.com/fchavelli/tsseg.git
cd tsseg
make install          # creates the conda env + installs tsseg
conda activate tsseg-env

Manual installation

conda env create -f environment.yml
conda activate tsseg-env
pip install -e .[all]

Lightweight install (no TensorFlow)

If TensorFlow causes issues on your platform, skip the [all] extra and install only the pieces you need:

conda env create -f environment.yml
conda activate tsseg-env
pip install -e .[torch,prophet,aeon]   # pick only the extras you want

Only TSCP2Detector requires TensorFlow (tsseg[tscp2]). All other 27+ algorithms work without it.

Optional extras

Most detectors work out of the box. Heavier dependencies are opt-in:

Extra	What it adds
tsseg[aeon]	Compatibility helpers for the aeon ecosystem
tsseg[prophet]	Facebook Prophet (ProphetDetector)
tsseg[patss]	Bayesian HSMM dependencies (PatssDetector)
tsseg[torch]	PyTorch-based detectors (TireDetector, Time2StateDetector)
tsseg[tglad]	PyTorch + NetworkX (TGLADDetector)
tsseg[tscp2]	TensorFlow + TCN layer (TSCP2Detector)
tsseg[accelerators]	Numba / Cython speedups
tsseg[docs]	Sphinx doc toolchain
tsseg[all]	Everything above

Contributing

make test       # run the test suite
make lint       # check style with ruff
make docs       # build the documentation locally

See the Contributing Guide for full instructions.

License

AGPLv3 — see the LICENSE file for details.

Third-party components

Several algorithms bundle adapted or vendored code under their own licenses:

Component	License	Source
aeon (base, EAgglo, Hidalgo, HMM, IGTS)	BSD-3	aeon-toolkit/aeon
ruptures/ (vendored v1.1.8)	BSD-2	deepcharles/ruptures
bocd/	Apache-2.0	hildensia/bayesian_changepoint_detection
clap/ (ClaSP / CLaP)	BSD-3	ermshaua/clasp
ggs/	BSD-2	cvxgrp/GGS
icid/	GPLv3	IsolationKernel/iCID
patss/	MIT	KU Leuven DTAI
tglad/vendor/ (uGLAD)	Non-Commercial	Harshs27/tGLAD
tirex/ (via tsseg-tirex)	NXAI Community License	NX-AI/tirex
autoplait/	not specified	Matsubara et al.
e2usd/	not specified	AI4CTS/E2Usd
espresso/	not specified	cruiseresearchgroup/ESPRESSO
ticc/	not specified	davidhallac/TICC
time2state/	not specified	Lab-ANT/Time2State
tire/	not specified	De Ryck, De Vos & Bertrand (KU Leuven)
tscp2/	not specified	Cruise Research Group

Each vendored directory contains a LICENSE file with full terms. The TiRex integration is built with technology from NXAI.