Adaptive Normalization for Forecasting Limit Order Book Data Using Convolutional Neural Networks

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  • Nikolaos Passalis, Tampere University
  • ,
  • Anastasios Tefas, Aristotle University of Thessaloniki
  • ,
  • Juho Kanniainen, Tampere University
  • ,
  • Moncef Gabbouj, Tampere University
  • ,
  • Alexandros Iosifidis

Deep learning models are capable of achieving state-of-the-art performance on a wide range of time series analysis tasks. However, their performance crucially depends on the employed normalization scheme, while they are usually unable to efficiently handle non-stationary features without first appropriately pre-processing them. These limitations impact the performance of deep learning models, especially when used for forecasting financial time series, due to their non-stationary and multimodal nature. In this paper we propose a data-driven adaptive normalization layer which is capable of learning the most appropriate normalization scheme that should be applied on the data. To this end, the proposed method first identifies the distribution from which the data were generated and then it dynamically shifts and scales them in order to facilitate the task at hand. The proposed nor-malization scheme is fully differentiable and it is trained in an end-to-end fashion along with the rest of the parameters of the model. The proposed method leads to significant performance improvements over several competitive normalization approaches, as demonstrated using a large-scale limit order book dataset.

OriginalsprogEngelsk
Titel2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
Antal sider5
ForlagIEEE
Udgivelsesår2020
Sider1713-1717
Artikelnummer9054321
ISBN (Elektronisk)9781509066315
DOI
StatusUdgivet - 2020
Begivenhed2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spanien
Varighed: 4 maj 20208 maj 2020

Konference

Konference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
LandSpanien
ByBarcelona
Periode04/05/202008/05/2020
SponsorThe Institute of Electrical and Electronics Engineers, Signal Processing Society

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