Passalis Nikolaos, Tefas Anastasios, Kanniainen Juho, Gabbouj Moncef, Iosifidis Alexandros
IEEE Trans Neural Netw Learn Syst. 2020 Sep;31(9):3760-3765. doi: 10.1109/TNNLS.2019.2944933. Epub 2019 Dec 18.
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 nonstationary and multimodal nature of the data pose significant challenges and severely affect the performance of DL models. In this brief, 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 backpropagation 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 retraining. The effectiveness of the proposed method is demonstrated using a large-scale limit order book data set, as well as a load forecasting data set.
深度学习（DL）模型可用于非常成功地处理时间序列分析任务。然而，如果数据没有进行适当的归一化处理，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峰性带来了重大挑战，并严重影响DL模型的性能。在本简报中，提出了一种简单但有效的神经层，它能够在考虑数据分布的同时对输入时间序列进行自适应归一化。所提出的层使用反向传播以端到端的方式进行训练，与其他评估的归一化方案相比，性能有显著提高。所提出的方法与传统归一化方法不同，因为它学习如何针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，而无需重新训练。使用大规模限价订单簿数据集以及负荷预测数据集证明了所提出方法的有效性。
