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MTS2Graph: Interpretable multivariate time series classification with temporal evolving graphs
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DOI：10.1016/j.patcog.2024.110486.png)
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Abstract
En 中文
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 work 1 , 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.
Keywords:
Multivariate time series
Interpretability
Neural networks
Classification
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