MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphs (2306.03834v1)

Abstract: 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 (CNN) have shown promising results in classifying Multivariate Time Series (MTS) 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. An essential criterion in understanding such predictive deep models involves quantifying the contribution of time-varying input variables to the classification. Hence, in this work, we introduce a new framework for interpreting multivariate time series data by extracting and clustering the input representative patterns that highly activate CNN neurons. This way, we identify each signal's role and dependencies, considering all possible combinations of signals in the MTS input. Then, we construct a graph that captures the temporal relationship between the extracted patterns for each layer. An effective graph merging strategy finds the connection of each node to the previous layer's nodes. Finally, a graph embedding algorithm generates new representations of the created interpretable time-series features. To evaluate the performance of our proposed framework, we run extensive experiments on eight datasets of the UCR/UEA archive, along with HAR and PAM datasets. The experiments indicate the benefit of our time-aware graph-based representation in MTS classification while enriching them with more interpretability.

• The paper presents a novel framework that transforms CNN-extracted features into temporal evolution graphs for interpretable multivariate time series classification.
• It constructs MHAP evolution graphs to capture dynamic relationships between high-activation subsequences, ensuring both accuracy and traceable decision-making.
• Empirical evaluations on UCR/UEA, HAR, and PAM datasets demonstrate competitive performance, bridging the gap between deep learning and interpretability in high-stakes applications.

An Assessment of MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphs

In the paper titled "MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphs," Younis et al. address the intricate challenge of multivariate time series (MTS) classification by proposing a novel framework that combines interpretability with high performance. This paper focuses on the demands of processing high-dimensional temporal data, often collected from diverse applications like medical care and activity recognition, where the relationships between multiple variables are critical yet challenging to decipher. Traditional methods like shapelets or bags of patterns are limited by computational complexity and lack interpretability. This research proposes a framework that leverages deep learning's expressive capabilities while ensuring the model's interpretability—a key requirement in sensitive applications, such as healthcare and finance.

Framework Outline and Key Contributions

MTS2Graph is a framework developed to improve the interpretability of MTS data classifications by transforming learned features from CNNs into temporal patterns or sequences. The main components of the MTS2Graph framework are as follows:

1. Extraction of Multivariate Highly Activated Periods (MHAP): The framework initiates by identifying and extracting subsequences of MTS that highly activate neurons within a CNN. This extraction considers every possible combination of MTS signals, thereby preserving the innate dependencies among variables within the dataset.
2. MHAP Evolution Graph Construction: The extracted MHAPs are used to construct a temporal graph that captures the chronological relationship between these periods. Each node in this graph represents a unique MHAP, and the directed edges specify the temporal sequence in which these patterns occur. This structure efficiently models the dynamic relationships among variables across time, aiding in subsequent feature interpretation and decision traceability.
3. Graph Embedding and Representation Learning: After forming the MHAP evolution graph, a graph embedding algorithm produces a latent representation space from the graph's structure. This space then forms the basis for further classification tasks, demonstrating high fidelity to the temporal dynamics of the original data.
4. Comprehensive Evaluation: The proposed methodology was tested across a series of datasets within the UCR/UEA archive as well as additional datasets like HAR and PAM. The empirical evaluation underlines the competitive performance of MTS2Graph not only in maintaining state-of-the-art accuracy but also in enhancing the interpretability of decision-making processes within CNN-based classifiers.

Practical and Theoretical Implications

From a practical standpoint, this research bridges a critical gap in the interpretability of deep neural networks used for MTS classification. The ability to trace and interpret decisions in high-stakes applications facilitates trust and reliability in automated systems. Furthermore, the temporal graph representations bear potential for use in other domains where sequence order critically impacts outcomes, such as genomic sequence analysis or complex sensor networks.

Theoretically, MTS2Graph challenges traditional paradigms about the trade-offs between model complexity and interpretability. By introducing a domain-agnostic structured approach that leverages the inherent correlations and temporal dependencies in MTS data, the authors offer a new perspective that may inform the design and evaluation of future neural network architectures.

Future Directions

Future research could expand on the MTS2Graph framework by integrating more advanced graph embedding techniques to further improve classification accuracy. Additionally, exploring the application of this framework to real-world data streams where temporal data sequences continue to evolve could provide insights into the model's adaptability and scalability. Lastly, a comparative analysis with other interpretability frameworks across different domains could cement MTS2Graph's applicability universal appeal.

In summary, this paper offers a significant contribution to the field of multivariate time series analysis by integrating interpretability and classification accuracy, fostering both technological advancement and societal trust in AI systems.