---
title: "Toward Interpretable Time Series Modeling: A Kernel Representation Perspective"
authors:
  - "Kunpeng Xu"
doi: "10.24963/ijcai.2025/1245"
url: "https://ijcai.org/proceedings/2025/1245"
pdf: "https://ijcai.org/proceedings/2025/1245.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10981-10982"
track: "Doctoral Consortium"
keywords:
  - "Data Mining: DM: Mining spatial and/or temporal data"
  - "Machine Learning: ML: Time series and data streams"
  - "Data Mining: DM: Mining data streams"
---

# Toward Interpretable Time Series Modeling: A Kernel Representation Perspective

Kunpeng Xu

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10981-10982. DOI: [10.24963/ijcai.2025/1245](https://doi.org/10.24963/ijcai.2025/1245)

## Abstract

Time series modeling is essential in finance, healthcare, and environmental science, yet nonlinear patterns, noise, and concept drift pose challenges. Although deep learning models, such as Transformer-based and recent pre-trained models, have achieved good performance across various time series tasks, they often lack interpretability, especially in co-evolving time series. This work introduces a kernel representation learning (KRL) perspective, rethinking time series modeling through kernel-induced self-representation to effectively capture temporal structures and dynamic transitions. Additionally, we establish theoretical connections between KRL and advanced deep-network models, demonstrating how kernel methods provide a principled approach to capturing complex time series behaviors.