Computer Science > Machine Learning
[Submitted on 28 Nov 2023 (v1), last revised 3 May 2024 (this version, v4)]
Title:FocusLearn: Fully-Interpretable, High-Performance Modular Neural Networks for Time Series
View PDF HTML (experimental)Abstract:Multivariate time series have many applications, from healthcare and meteorology to life science. Although deep learning models have shown excellent predictive performance for time series, they have been criticised for being "black-boxes" or non-interpretable. This paper proposes a novel modular neural network model for multivariate time series prediction that is interpretable by construction. A recurrent neural network learns the temporal dependencies in the data while an attention-based feature selection component selects the most relevant features and suppresses redundant features used in the learning of the temporal dependencies. A modular deep network is trained from the selected features independently to show the users how features influence outcomes, making the model interpretable. Experimental results show that this approach can outperform state-of-the-art interpretable Neural Additive Models (NAM) and variations thereof in both regression and classification of time series tasks, achieving a predictive performance that is comparable to the top non-interpretable methods for time series, LSTM and XGBoost.
Submission history
From: Qiqi Su [view email][v1] Tue, 28 Nov 2023 14:51:06 UTC (2,299 KB)
[v2] Wed, 29 Nov 2023 13:23:42 UTC (2,299 KB)
[v3] Mon, 18 Mar 2024 17:39:11 UTC (2,299 KB)
[v4] Fri, 3 May 2024 16:44:31 UTC (2,252 KB)
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