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arXiv · 2410.10535

Transparent Networks for Multivariate Time Series

Minkyu Kim·Suan Lee·Jinho Kim
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Abstract

Transparent models, which provide inherently interpretable predictions, are receiving significant attention in high-stakes domains. However, despite much real-world data being collected as time series, there is a lack of studies on transparent time series models. To address this gap, we propose a novel transparent neural network model for time series called Generalized Additive Time Series Model (GATSM). GATSM consists of two parts: 1) independent feature networks to learn feature representations, and 2) a transparent temporal module to learn temporal patterns across different time steps using the feature representations. This structure allows GATSM to effectively capture temporal patterns and handle varying-length time series while preserving transparency. Empirical experiments show that GATSM significantly outperforms existing generalized additive models and achieves comparable performance to black-box time series models, such as recurrent neural networks and Transformer. In addition, we demonstrate that GATSM finds interesting patterns in time series.

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BibTeXRIS

Minkyu Kim, Suan Lee, Jinho Kim. 2024-10-14. Transparent Networks for Multivariate Time Series. https://arxiv.org/abs/2410.10535

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