{"ID":2866815,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.20595","arxiv_id":"2509.20595","title":"TSKAN: Interpretable Machine Learning for QoE modeling over Time Series Data","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.","short_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 seri...","url_abs":"https://arxiv.org/abs/2509.20595","url_pdf":"https://arxiv.org/pdf/2509.20595v1","authors":"[\"Kamal Singh\",\"Priyanka Rawat\",\"Sami Marouani\",\"Baptiste Jeudy\"]","published":"2025-09-24T22:25:30Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.NI\"]","methods":"[]","has_code":false}