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Data-driven Neural Architecture Learning For Financial Time-series Forecasting

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  • Dat Thanh Tran
  • Juho Kanniainen
  • Moncef Gabbouj
  • Alexandros Iosifidis

Abstract

Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies. In addition, different data modalities often embed different nonlinear relationships which are difficult to capture by human-designed models. To tackle the supervised learning task in financial time-series prediction, we propose the application of a recently formulated algorithm that adaptively learns a mapping function, realized by a heterogeneous neural architecture composing of Generalized Operational Perceptron, given a set of labeled data. With a modified objective function, the proposed algorithm can accommodate the frequently observed imbalanced data distribution problem. Experiments on a large-scale Limit Order Book dataset demonstrate that the proposed algorithm outperforms related algorithms, including tensor-based methods which have access to a broader set of input information.

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  • Dat Thanh Tran & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Data-driven Neural Architecture Learning For Financial Time-series Forecasting," Papers 1903.06751, arXiv.org.
    • Handle: RePEc:arx:papers:1903.06751
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    File URL: http://arxiv.org/pdf/1903.06751
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    References listed on IDEAS

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    1. Dat Thanh Tran & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2017. "Tensor Representation in High-Frequency Financial Data for Price Change Prediction," Papers 1709.01268, arXiv.org, revised Nov 2017.
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    Cited by:

    1. Ilia Zaznov & Julian Kunkel & Alfonso Dufour & Atta Badii, 2022. "Predicting Stock Price Changes Based on the Limit Order Book: A Survey," Mathematics, MDPI, vol. 10(8), pages 1-33, April.

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