N-BEATS Perceiver: A Novel Approach for Robust Cryptocurrency Portfolio Forecasting

Attilio Sbrana () and Paulo André Lima de Castro ()
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Attilio Sbrana: Aeronautics Institute of Technology (ITA)
Paulo André Lima de Castro: Aeronautics Institute of Technology (ITA)

Computational Economics, 2024, vol. 64, issue 2, No 14, 1047-1081

Abstract: Abstract In this paper, we propose a novel approach for forecasting cryptocurrency portfolios, harnessing modified versions of the N-BEATS deep learning architecture, integrated with convolutional network layers, Transformer mechanisms, and the Mish activation function. Our thorough evaluation, featuring an extensive sample size exceeding 4 million portfolio test samples, shows these variations outperforming traditional and other deep learning forecasting methods across various metrics. Particularly noteworthy is our N-BEATS Perceiver model, a Transformer-based variation, which not only delivers superior forecast accuracy but also exhibits a robust risk profile with less downside. Furthermore, the model performs exceptionally well under the TOPSIS method across a broad spectrum of portfolio evaluation parameters, making it a valuable asset for both portfolio selection and risk management in the dynamic cryptocurrency market.

Keywords: N-BEATS; Perceiver; Transformers; Deep learning; Forecasting; Cryptocurrency (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10470-8

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