TSKAN: Interpretable Machine Learning for QoE modeling over Time Series Data

Kamal Singh; Priyanka Rawat; Sami Marouani; Baptiste Jeudy

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arXiv · 2509.20595

TSKAN: Interpretable Machine Learning for QoE modeling over Time Series Data

Kamal Singh·Priyanka Rawat·Sami Marouani·Baptiste Jeudy

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Abstract

Quality of Experience (QoE) modeling is crucial for optimizing video streaming services to capture the complex relationships between different features and user experience. We propose a novel approach to QoE modeling in video streaming applications using interpretable Machine Learning (ML) techniques over raw time series data. Unlike traditional black-box approaches, our method combines Kolmogorov-Arnold Networks (KANs) as an interpretable readout on top of compact frequency-domain features, allowing us to capture temporal information while retaining a transparent and explainable model. We evaluate our method on popular datasets and demonstrate its enhanced accuracy in QoE prediction, while offering transparency and interpretability.

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Kamal Singh, Priyanka Rawat, Sami Marouani, Baptiste Jeudy. 2025-09-24. TSKAN: Interpretable Machine Learning for QoE modeling over Time Series Data. https://arxiv.org/abs/2509.20595

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