TY - JOUR

T1 - MTS2Graph

T2 - Interpretable multivariate time series classification with temporal evolving graphs

AU - Younis, Raneen

AU - Hakmeh, Abdul

AU - Ahmadi, Zahra

N1 - Funding Information: This research was partially funded by the Federal Ministry of Education and Research (BMBF), Germany, under the project LeibnizKILabor with grant No. 01DD20003.

PY - 2024/4/8

Y1 - 2024/4/8

N2 - Conventional time series classification approaches based on bags of patterns or shapelets face significant challenges in dealing with a vast amount of feature candidates from high-dimensional multivariate data. In contrast, deep neural networks can learn low-dimensional features efficiently, and in particular, convolutional neural networks have shown promising results in classifying multivariate time series data. A key factor in the success of deep neural networks is this astonishing expressive power. However, this power comes at the cost of complex, black-boxed models, conflicting with the goals of building reliable and human-understandable models. In this work1 , we introduce a new interpretable framework for multivariate time series data that by extracting and clustering the input quantifies the contribution of time-varying input variables and each signal's role to the classification. We construct a graph that captures the temporal relationship between the extracted patterns for each layer and propose an effective merging strategy to aggregate those graphs into one. Finally, a graph embedding algorithm generates new representations of the created interpretable time-series features. Our extensive experiments indicate the benefit of our time-aware graph-based representation in multivariate time series classification while enriching them with more interpretability.

AB - Conventional time series classification approaches based on bags of patterns or shapelets face significant challenges in dealing with a vast amount of feature candidates from high-dimensional multivariate data. In contrast, deep neural networks can learn low-dimensional features efficiently, and in particular, convolutional neural networks have shown promising results in classifying multivariate time series data. A key factor in the success of deep neural networks is this astonishing expressive power. However, this power comes at the cost of complex, black-boxed models, conflicting with the goals of building reliable and human-understandable models. In this work1 , we introduce a new interpretable framework for multivariate time series data that by extracting and clustering the input quantifies the contribution of time-varying input variables and each signal's role to the classification. We construct a graph that captures the temporal relationship between the extracted patterns for each layer and propose an effective merging strategy to aggregate those graphs into one. Finally, a graph embedding algorithm generates new representations of the created interpretable time-series features. Our extensive experiments indicate the benefit of our time-aware graph-based representation in multivariate time series classification while enriching them with more interpretability.

KW - Classification

KW - Interpretability

KW - Multivariate time series

KW - Neural networks

UR - http://www.scopus.com/inward/record.url?scp=85190319516&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2306.03834

DO - 10.48550/arXiv.2306.03834

M3 - Article

AN - SCOPUS:85190319516

VL - 152

JO - Pattern recognition

JF - Pattern recognition

SN - 0031-3203

M1 - 110486

ER -