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.

翻译：数据预处理是任何机器学习流程中的关键环节，对模型性能与训练效率具有显著影响。这一现象在使用深度神经网络进行时间序列预测与分类时尤为突出：真实世界的时间序列数据常呈现出多模态性、偏态分布及异常值等不规则特征，若未妥善处理这些特性，模型性能将急剧下降。本文提出EDAIN（扩展深度自适应输入归一化）层——一种新颖的自适应神经层，它能够以端到端的方式学习如何针对特定任务对不规则时间序列数据进行恰当归一化，而非采用固定归一化方案。该层通过反向传播与深度神经网络同步优化其未知参数实现这一目标。我们在合成数据、信用违约预测数据集以及大规模限价订单簿基准数据集上开展的实验表明，相较传统归一化方法及现有自适应时间序列预处理层，EDAIN层展现出更优越的性能。