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

• URL: http://arxiv.org/abs/2310.14720v2
• Date: Thu, 29 Feb 2024 08:30:03 GMT
• Title: Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks
• Authors: Marcus A. K. September, Francesco Sanna Passino, Leonie Goldmann, Anton Hinel
• Abstract summary: We propose the EDAIN layer, a novel adaptive neural layer that learns how to appropriately normalize irregular time series data for a given task in an end-to-end fashion. Our experiments, conducted using synthetic data, a credit default prediction dataset, and a large-scale limit order book benchmark dataset, demonstrate the superior performance of the EDAIN layer.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data preprocessing is a crucial part of any machine learning pipeline, and it can have a significant impact on both performance and training efficiency. This is especially evident when using deep neural networks for time series prediction and classification: real-world time series data often exhibit irregularities such as multi-modality, skewness and outliers, and the model performance can degrade rapidly if these characteristics are not adequately addressed. In this work, we propose the EDAIN (Extended Deep Adaptive Input Normalization) layer, a novel adaptive neural layer that learns how to appropriately normalize irregular time series data for a given task in an end-to-end fashion, instead of using a fixed normalization scheme. This is achieved by optimizing its unknown parameters simultaneously with the deep neural network using back-propagation. Our experiments, conducted using synthetic data, a credit default prediction dataset, and a large-scale limit order book benchmark dataset, demonstrate the superior performance of the EDAIN layer when compared to conventional normalization methods and existing adaptive time series preprocessing layers.

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