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  A hybrid dual-branch model with recurrence plots and transposed transformer for stock trend prediction

Su, J., Li, H., Wang, R., Guo, W., Hao, Y., Kurths, J., Gao, Z. (2025): A hybrid dual-branch model with recurrence plots and transposed transformer for stock trend prediction. - Chaos, 35, 1, 013125.
https://doi.org/10.1063/5.0233275

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Datensatz-Permalink: https://publications.pik-potsdam.de/pubman/item/item_32073 Versions-Permalink: https://publications.pik-potsdam.de/pubman/item/item_32073_2
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Su, Jingyu1, Autor
Li, Haoyu1, Autor
Wang, Ruiqi1, Autor
Guo, Wei1, Autor
Hao, Yushi1, Autor
Kurths, Jürgen2, Autor              
Gao, Zhongke1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: Stock trend prediction is a significant challenge due to the inherent uncertainty and complexity of stock market time series. In this study, we introduce an innovative dual-branch network model designed to effectively address this challenge. The first branch constructs recurrence plots (RPs) to capture the nonlinear relationships between time points from historical closing price sequences and computes the corresponding recurrence quantifification analysis measures. The second branch integrates transposed transformers to identify subtle interconnections within the multivariate time series derived from stocks. Features extracted from both branches are concatenated and fed into a fully connected layer for binary classification, determining whether the stock price will rise or fall the next day. Our experimental results based on historical data from seven randomly selected stocks demonstrate that our proposed dual-branch model achieves superior accuracy (ACC) and F1-score compared to traditional machine learning and deep learning approaches. These findings underscore the efficacy of combining RPs with deep learning models to enhance stock trend prediction, offering considerable potential for refining decision-making in financial markets and investment strategies.

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Sprache(n): eng - Englisch
 Datum: 2025-01-102025-01-10
 Publikationsstatus: Final veröffentlicht
 Seiten: 10
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1063/5.0233275
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
 Art des Abschluß: -

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Titel: Chaos
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
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Seiten: - Band / Heft: 35 (1) Artikelnummer: 013125 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808
Publisher: American Institute of Physics (AIP)