Transparent Networks for Multivariate Time Series

Minkyu Kim, Suan Lee, Jinho Kim

Keywords: Generalized Additive Models, Interpretability, Time Series, Transparent Model

TL;DR: We propose a novel transparent model for time series.

Abstract: Transparent models, which are machine learning models that produce 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 dynamic-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. The source code is available at https://anonymous.4open.science/r/GATSM-78F4/.

Primary Area: Interpretability and explainability

Submission Number: 10099