A Network Science Approach to Granular Time Series Segmentation

TL;DR

This paper introduces a novel granular time series segmentation method using graph neural networks, transforming time series into graphs to improve segmentation accuracy and reduce data requirements.

Contribution

The paper formulates TSS as a node classification problem on graphs, analyzing various TS-to-graph transformations with GNNs, and demonstrates superior performance over existing methods.

Findings

Achieves an average F1 score of 0.97 across 59 datasets

Outperforms the seq2point baseline by 0.05 in F1 score

Reduces training data needs compared to baseline methods

Abstract

Time series segmentation (TSS) is one of the time series (TS) analysis techniques, that has received considerably less attention compared to other TS related tasks. In recent years, deep learning architectures have been introduced for TSS, however their reliance on sliding windows limits segmentation granularity due to fixed window sizes and strides. To overcome these challenges, we propose a new more granular TSS approach that utilizes the Weighted Dual Perspective Visbility Graph (WDPVG) TS into a graph and combines it with a Graph Attention Network (GAT). By transforming TS into graphs, we are able to capture different structural aspects of the data that would otherwise remain hidden. By utilizing the representation learning capabilities of Graph Neural Networks, our method is able to effectively identify meaningful segments within the TS. To better understand the potential of our approach, we also experimented with different TS-to-graph transformations and compared their performance. Our contributions include: a) formulating the TSS as a node classification problem on graphs; b) conducting an extensive analysis of various TS-to-graph transformations applied to TSS using benchmark datasets from the TSSB repository; c) providing the first detailed study on utilizing GNNs for analyzing graph representations of TS in the context of TSS; d) demonstrating the effectiveness of our method, which achieves an average F1 score of 0.97 across 59 diverse TSS benchmark datasets; e) outperforming the seq2point baseline method by 0.05 in terms of F1 score; and f) reducing the required training data compared to the baseline methods.