TSKAN: Interpretable Machine Learning for QoE modeling over Time Series Data
This addresses the need for transparent QoE modeling in video streaming services, offering an incremental improvement over traditional black-box methods.
The paper tackled the problem of modeling Quality of Experience (QoE) for video streaming by proposing an interpretable machine learning approach using Kolmogorov-Arnold Networks (KANs) on time series data, resulting in enhanced accuracy in QoE prediction while maintaining transparency.
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.