xailib.xailib_ts

Time series data explainability classes for XAI-Lib.

This module provides base classes for explaining predictions on time series data. It extends the base Explainer and Explanation classes with time series-specific functionality.

Time series explanations typically highlight which time steps or temporal patterns contributed most to a model’s prediction. Common use cases include:

• Anomaly detection explanation

• Time series classification explanation

• Forecasting explanation

Classes:

TSExplainer: Base class for time series data explainers. TSExplanation: Base class for time series data explanations.

Example

Using LASTS for time series explanation:

from xailib.explainers.lasts_explainer import LastsExplainer
from xailib.models.keras_ts_classifier_wrapper import KerasTSClassifierWrapper

# Wrap your model
bb = KerasTSClassifierWrapper(your_ts_model)

# Create and fit explainer
explainer = LastsExplainer(bb)
explainer.fit(X_train, y_train, config)

# Generate explanation
explanation = explainer.explain(time_series)

See also

xailib.explainers.lasts_explainer: LASTS implementation for time series.

Note

Time series explanation support is currently being expanded. Additional methods will be added in future releases.

Classes

TSExplainer()	Abstract base class for time series data explainers.
TSExplanation()	Abstract base class for time series data explanations.