xaitimesynth 0.1.1

Generate synthetic time series with known ground truth feature locations for XAI evaluation

xaitimesynth

A Python package for benchmarking explainable AI (XAI) algorithms (mostly feature attributions) on time series classification tasks using synthetic data with known ground truth feature locations.

Why xaitimesynth?

Evaluating XAI methods for time series is challenging because we rarely know which time points truly matter for classification. xaitimesynth solves this by generating synthetic data where you control exactly where the class-discriminating features are located.

Each synthetic time series follows a simple additive model: x = background + feature, where the feature is placed at a known window. This lets you directly measure whether attribution methods correctly identify the important time points.

Key Features

• Known ground truth: Feature locations are tracked internally, enabling direct evaluation of attribution correctness
• Flexible data generation: Combine signals (random walks, seasonal patterns, noise) with localized features (peaks, level shifts, pulses)
• Univariate and multivariate: Generate single-channel or multi-channel time series
• Fluent builder API: Chain methods to define complex datasets concisely
• YAML configuration: Define datasets in config files for reproducibility
• Built-in metrics: AUC-PR, AUC-ROC, Relevance Mass Accuracy, Relevance Rank Accuracy, and more

Installation

pip install xaitimesynth

Quick Start

from xaitimesynth import TimeSeriesBuilder, gaussian_noise, gaussian_pulse, seasonal
from xaitimesynth.metrics import auc_pr_score, relevance_mass_accuracy
import numpy as np

# define dataset structure once
base_builder = (
    TimeSeriesBuilder(n_timesteps=100, normalization="zscore")
    .for_class(0)
    .add_signal(gaussian_noise(sigma=1.0))
    .add_feature(gaussian_pulse(amplitude=3.0), random_location=True, length_pct=0.3)
    .for_class(1)
    .add_signal(gaussian_noise(sigma=1.0))
    .add_feature(
        seasonal(period=10, amplitude=3.0), random_location=True, length_pct=0.3
    )
)

# generate train and test sets with different seeds
train = base_builder.clone(n_samples=200, random_state=42).build()
test = base_builder.clone(n_samples=50, random_state=43).build()

# visualise instances from created dataset (by default first observation from each class)
plot = plot_components(train)
plot.show()

# Replace with your XAI method output; shape must be (n_samples, n_dims, n_timesteps)
attributions = np.random.rand(*test["X"].shape)

# Evaluate against ground truth feature locations
auc = auc_pr_score(attributions, test, normalize=True)
rma = relevance_mass_accuracy(attributions, test)

Documentation

Full documentation is available at gregorbaer.github.io/xaitimesynth.

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

Citation

If you use xaitimesynth in your work, please consider citing:

Baer, G. (2026). xaitimesynth: A Python Package for Evaluating Attribution Methods for Time Series with Synthetic Ground Truth (arXiv:2603.06781). arXiv. https://doi.org/10.48550/arXiv.2603.06781

License

This project is licensed under the MIT License - see the LICENSE file for details.