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Multivariate time series have many applications, from healthcare andmeteorology to life science. Although deep learning models have shown excellentpredictive performance for time series, they have been criticised for being"black-boxes" or non-interpretable. This paper proposes a novel modular neuralnetwork model for multivariate time series prediction that is interpretable byconstruction. A recurrent neural network learns the temporal dependencies inthe data while an attention-based feature selection component selects the mostrelevant features and suppresses redundant features used in the learning of thetemporal dependencies. A modular deep network is trained from the selectedfeatures independently to show the users how features influence outcomes,making the model interpretable. Experimental results show that this approachcan outperform state-of-the-art interpretable Neural Additive Models (NAM) andvariations thereof in both regression and classification of time series tasks,achieving a predictive performance that is comparable to the topnon-interpretable methods for time series, LSTM and XGBoost.

Modular Neural Networks for Time Series Forecasting: Interpretability and Feature Selection using Attention