英文标题：
《Deep Adaptive Input Normalization for Time Series Forecasting》
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作者：
Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj,
  Alexandros Iosifidis
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最新提交年份：
2019
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英文摘要：
  Deep Learning (DL) models can be used to tackle time series analysis tasks with great success. However, the performance of DL models can degenerate rapidly if the data are not appropriately normalized. This issue is even more apparent when DL is used for financial time series forecasting tasks, where the non-stationary and multimodal nature of the data pose significant challenges and severely affect the performance of DL models. In this work, a simple, yet effective, neural layer, that is capable of adaptively normalizing the input time series, while taking into account the distribution of the data, is proposed. The proposed layer is trained in an end-to-end fashion using back-propagation and leads to significant performance improvements compared to other evaluated normalization schemes. The proposed method differs from traditional normalization methods since it learns how to perform normalization for a given task instead of using a fixed normalization scheme. At the same time, it can be directly applied to any new time series without requiring re-training. The effectiveness of the proposed method is demonstrated using a large-scale limit order book dataset, as well as a load forecasting dataset.
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中文摘要：
深度学习（DL）模型可以成功地用于处理时间序列分析任务。然而，如果数据没有得到适当的规范化，DL模型的性能可能会迅速退化。当DL用于金融时间序列预测任务时，这个问题更加明显，因为数据的非平稳性和多模态性带来了重大挑战，并严重影响了DL模型的性能。在这项工作中，提出了一种简单而有效的神经层，该神经层能够自适应地规范化输入时间序列，同时考虑数据的分布。该层采用反向传播以端到端的方式进行训练，与其他经过评估的规范化方案相比，该层的性能有了显著的提高。该方法不同于传统的归一化方法，因为它学习如何对给定任务执行归一化，而不是使用固定的归一化方案。同时，它可以直接应用于任何新的时间序列，而无需重新培训。通过一个大型限价订单数据集和一个负荷预测数据集验证了该方法的有效性。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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