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Regression problems with time-series predictors are common in banking andmany other areas of application. In this paper, we use multi-head attentionnetworks to develop interpretable features and use them to achieve goodpredictive performance. The customized attention layer explicitly usesmultiplicative interactions and builds feature-engineering heads that capturetemporal dynamics in a parsimonious manner. Convolutional layers are used tocombine multivariate time series. We also discuss methods for handling staticcovariates in the modeling process. Visualization and explanation tools areused to interpret the results and explain the relationship between the inputsand the extracted features. Both simulation and real dataset are used toillustrate the usefulness of the methodology. Keyword: Attention heads, Deepneural networks, Interpretable feature engineering

Interpretable Feature Engineering for Time Series Predictors using Attention Networks