WormKAN: Are KAN Effective for Identifying and Tracking Concept Drift in Time Series?

TL;DR

WormKAN is a novel KAN-based model designed to detect and track concept drift in time series by capturing local dependencies, learning robust representations, and identifying structural shifts through concept dynamics.

Contribution

This paper introduces WormKAN, the first model leveraging Kolmogorov-Arnold Networks for effective concept drift detection and tracking in co-evolving time series.

Findings

KAN-based models effectively segment time series into meaningful concepts

WormKAN accurately detects structural shifts and concept transitions

Experimental results demonstrate improved drift tracking performance

Abstract

Dynamic concepts in time series are crucial for understanding complex systems such as financial markets, healthcare, and online activity logs. These concepts help reveal structures and behaviors in sequential data for better decision-making and forecasting. However, existing models often struggle to detect and track concept drift due to limitations in interpretability and adaptability. To address this challenge, inspired by the flexibility of the recent Kolmogorov-Arnold Network (KAN), we propose WormKAN, a concept-aware KAN-based model to address concept drift in co-evolving time series. WormKAN consists of three key components: Patch Normalization, Temporal Representation Module, and Concept Dynamics. Patch normalization processes co-evolving time series into patches, treating them as fundamental modeling units to capture local dependencies while ensuring consistent scaling. The temporal representation module learns robust latent representations by leveraging a KAN-based autoencoder, complemented by a smoothness constraint, to uncover inter-patch correlations. Concept dynamics identifies and tracks dynamic transitions, revealing structural shifts in the time series through concept identification and drift detection. These transitions, akin to passing through a \textit{wormhole}, are identified by abrupt changes in the latent space. Experiments show that KAN and KAN-based models (WormKAN) effectively segment time series into meaningful concepts, enhancing the identification and tracking of concept drift.