FocusLearn: Fully-Interpretable, High-Performance Modular Neural Networks for Time Series

Qiqi Su, Christos Kloukinas, Artur d'Avila Garcez

Abstract: Multivariate time series have had many applications in areas from healthcare and finance to meteorology and life sciences. Although deep neural networks have shown excellent predictive performance for time series, they have been criticised for being non-interpretable. Neural Additive Models, however, are known to be fully-interpretable by construction, but may achieve far lower predictive performance than deep networks when applied to time series. This paper introduces FocusLearn, a fully-interpretable modular neural network capable of matching or surpassing the predictive performance of deep networks trained on multivariate time series. In FocusLearn, a recurrent neural network learns the temporal dependencies in the data, while a multi-headed attention layer learns to weight selected features while also suppressing redundant features. Modular neural networks are then trained in parallel and independently, one for each selected feature. This modular approach allows the user to inspect how features influence outcomes in the exact same way as with additive models. Experimental results show that this new approach outperforms additive models in both regression and classification of time series tasks, achieving predictive performance that is comparable to state-of-the-art, non-interpretable deep networks applied to time series.