Time-Series based xAI Methods

Introduces explainability methods tailored for sequential and time-series data.

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Suggested citation

Misheva, Branka Hadji (2025). Time-Series based xAI Methods. Q2 ecosystem. Available at https://quantinar.com/course/944/time-series-based-xai-method or http://dx.doi.org/10.24818/IDA-CL/2025.97.

@misc{misheva2025, author = {Misheva, Branka Hadji}, title = {Time-Series based xAI Methods}, year = {2025}, howpublished = {Q2 ecosystem}, doi = {http://dx.doi.org/10.24818/IDA-CL/2025.97.}, url = {https://quantinar.com/course/944/time-series-based-xai-method} }
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	Time-Series based xAI Methods (pdf)	3.76 M

Requirements

1) Foundations in Machine Learning – understanding of supervised learning, model training, overfitting, feature importance. 2) Mathematical Background – basic linear algebra, probability, and statistics (enough to follow Shapley values and regression concepts). 3) Programming Skills – ability to work with Python (libraries such as scikit-learn, XGBoost, SHAP, LIME).

General Overview

Description

This session introduces explainability approaches tailored to sequential and time-dependent data, where relationships unfold across time rather than in static snapshots.You will gain skills in understanding temporal dependencies and interpreting models used in forecasting and sequence analysis.