tsseg.algorithms.tire package

TIRE — Time-Invariant Representation for change point detection.

Description

TIRE learns a compact representation by training ensembles of parallel autoencoders in both the time domain (TD) and frequency domain (FD). The shared latent dimensions across autoencoders capture the time-invariant signal statistics while the private dimensions capture time-varying noise. A dissimilarity curve is computed from the shared representations and peaks in this curve indicate change points.

By tuning the loss weights and latent dimensions for each domain you can control whether the detector focuses on amplitude changes (TD) or spectral changes (FD) or both.

Type: change point detection

Supervision: unsupervised or semi-supervised

Scope: univariate and multivariate

Requires: PyTorch

Parameters

Name	Type	Default	Description
window_size	int	20	Sliding window size (>= 4).
stride	int	1	Stride between consecutive windows.
domain	str	"both"	Domain(s) for representation learning ("TD", "FD", "both").
intermediate_dim_td	int	0	Hidden dim of TD autoencoder (0 = skip intermediate layer).
latent_dim_td	int	1	Latent dim of TD autoencoder.
nr_shared_td	int	1	Number of shared latent dims (TD).
nr_ae_td	int	3	Number of parallel TD autoencoders.
loss_weight_td	float	1.0	Loss weight for TD component.
intermediate_dim_fd	int	10	Hidden dim of FD autoencoder.
latent_dim_fd	int	1	Latent dim of FD autoencoder.
nr_shared_fd	int	1	Number of shared latent dims (FD).
nr_ae_fd	int	3	Number of parallel FD autoencoders.
loss_weight_fd	float	1.0	Loss weight for FD component.
nfft	int	30	FFT size for frequency-domain windows.
norm_mode	str	"timeseries"	Normalisation scope ("window" or "timeseries").
peak_distance_fraction	float	0.01	Min fraction of series length between peaks.
max_epochs	int	20	Maximum training epochs.
patience	int	5	Early-stopping patience.
learning_rate	float	1e-3	Optimiser learning rate.
n_segments	int / None	None	Number of segments (overrides peak detection).
axis	int	0	Time axis.
random_state	int / None	None	Random seed.

Usage

from tsseg.algorithms import TireDetector

detector = TireDetector(window_size=30, domain="both", n_segments=5)
labels = detector.fit_predict(X)

Implementation: Origin: new code.

Reference: De Ryck, De Vos, Bertrand & Verhoest (2021), Change Point Detection in Time Series Data Using Autoencoders with a Time-Invariant Representation, IEEE TSIPN.

Submodules

tsseg.algorithms.tire.detector module

tsseg.algorithms.tire.utils module

Module contents