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.

翻译：多变量时间序列在医疗、气象及生命科学等领域具有广泛应用。尽管深度学习模型在时间序列预测中展现出卓越性能，但其因被视为"黑箱"或缺乏可解释性而备受批评。本文提出一种新型模块化神经网络模型，该模型通过结构设计实现多变量时间序列预测的可解释性。其中，循环神经网络学习数据中的时间依赖性，而基于注意力的特征选择组件可筛选最相关特征并抑制冗余特征对时间依赖性学习的影响。通过独立训练基于选定特征的模块化深度网络，模型可向用户展示特征对预测结果的影响机制，从而实现可解释性。实验结果表明，该方法在时间序列回归与分类任务中均能超越现有可解释性基准模型——神经加性模型（NAM）及其变体，其预测性能可与顶级非可解释性方法LSTM和XGBoost相媲美。