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Data preprocessing is a crucial part of any machine learning pipeline, and itcan have a significant impact on both performance and training efficiency. Thisis especially evident when using deep neural networks for time seriesprediction and classification: real-world time series data often exhibitirregularities such as multi-modality, skewness and outliers, and the modelperformance can degrade rapidly if these characteristics are not adequatelyaddressed. In this work, we propose the EDAIN (Extended Deep Adaptive InputNormalization) layer, a novel adaptive neural layer that learns how toappropriately normalize irregular time series data for a given task in anend-to-end fashion, instead of using a fixed normalization scheme. This isachieved by optimizing its unknown parameters simultaneously with the deepneural network using back-propagation. Our experiments, conducted usingsynthetic data, a credit default prediction dataset, and a large-scale limitorder book benchmark dataset, demonstrate the superior performance of the EDAINlayer when compared to conventional normalization methods and existing adaptivetime series preprocessing layers.

Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks