Deep Adaptive Input Normalization for Time Series Forecasting

Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis

Research output: Contribution to journalArticleScientificpeer-review


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.
Original languageEnglish
Pages (from-to)3760 - 3765
Number of pages6
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number9
Publication statusE-pub ahead of print - 18 Dec 2019
Publication typeA1 Journal article-refereed


  • Data normalization
  • deep learning (DL)
  • limit order book data
  • time series forecasting.

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  • Publication forum level 3


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