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

Universit e Jean Monnet Saint-Etienne, T elecom Saint-Etienne, F-42023 Saint-Etienne, France Email: {firstname.surname}@univ-st-etienne.fr 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. Quality of Experience (QoE) is a crucial aspect in today's digital landscape, as it directly affects how users perceive and interact with applications and services. Defined as'the overall acceptability of an application or service, as perceived subjectively by the end user' [1], QoE is a complex and multifaceted concept that cannot be solely defined by traditional Quality of Service (QoS) metrics such as bandwidth, delay, or jitter. In the context of video streaming, for instance, perceptual quality is a critical component of QoE.