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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.20595 |
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| _version_ | 1866908558031847424 |
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| author | Singh, Kamal Rawat, Priyanka Marouani, Sami Jeudy, Baptiste |
| author_facet | Singh, Kamal Rawat, Priyanka Marouani, Sami Jeudy, Baptiste |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20595 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TSKAN: Interpretable Machine Learning for QoE modeling over Time Series Data Singh, Kamal Rawat, Priyanka Marouani, Sami Jeudy, Baptiste Machine Learning Networking and Internet Architecture 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. |
| title | TSKAN: Interpretable Machine Learning for QoE modeling over Time Series Data |
| topic | Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2509.20595 |