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FusionLSTM-CNF: a confidence-calibrated multi-modal late fusion framework for robust stock movement prediction under uncertainty.

Overview

Authors: Tian Wen Wang1, Zaffar Ahmed Shaikh2,3, Sook Lu Yong4, Hela Elmannai5, Lip Yee Por6
  1. Institute for Advanced Studies (IAS), Universiti Malaya,Kuala Lumpur, Wilayah Persekutuan Malaysia
  2. Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari,Karachi, 75660 Pakistan
  3. School of Engineering, École Polytechnique Fédérale de Lausanne,1015 Lausanne, Switzerland
  4. Department of Economics, Faculty of Business and Economics, Universiti Malaya,Kuala Lumpur, Wilayah Persekutuan Malaysia
  5. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University,11671 Riyadh, Saudi Arabia
  6. Center of Research for Cyber Security and Network (CSNET), Faculty of Computer Science and Information Technology, Universiti Malaya,Kuala Lumpur, Malaysia
Institutions: University of Malaya (Malaysia); Shaheed Benazir Bhutto University (Pakistan); École Polytechnique Fédérale de Lausanne (Switzerland); Princess Nourah bint Abdulrahman University (Saudi Arabia)
Journal: Scientific reports, volume 16, issue 1, article 17494
Dates: received 12 October 2025; accepted 4 March 2026; published online 9 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-43381-3 · PMID 41957129 · PMCID PMC13237071 · OpenAlex W7152597961
Open access: gold, a free copy (OpenAlex)
Status: empty repository
Methods: Connectivity, Machine learning, Smoothing, state filtering, decompositions
Keywords: Deep learning, Financial prediction, Multi-modal fusion, LSTM networks, Uncertainty quantification, Confidence calibration, Time series analysis, Engineering, Mathematics and computing
Topic: Stock Market Forecasting Methods (Management Science and Operations Research, Decision Sciences), according to OpenAlex
Funding: Princess Nourah Bint Abdulrahman University (PNURSP2026R747)
Citations: not cited yet (Europe PMC); 36 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

veecky29/fusionlstm_cnf

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 0 files, 0 scripts
Software Heritage: not archived
Found in: the text, “Implementation details and training protocol”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
At the source: github.com/Veecky29/FusionLSTM_CNF/issues/1

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Data

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Data availability statement

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Read it in the paper: doi.org/10.1038/s41598-026-43381-3.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 9 keywords, 1 funder, 3 references.

Cite

This paper

Wang, T. W., Shaikh, Z. A., Yong, S. L., Elmannai, H., & Por, L. Y. (2026). FusionLSTM-CNF: a confidence-calibrated multi-modal late fusion framework for robust stock movement prediction under uncertainty. Scientific reports, 16(1), 17494. https://doi.org/10.1038/s41598-026-43381-3

BibTeX

@article{wang2026fusionlstm,
author = {Wang, Tian Wen and Shaikh, Zaffar Ahmed and Yong, Sook Lu and Elmannai, Hela and Por, Lip Yee},
title = {{FusionLSTM-CNF: a confidence-calibrated multi-modal late fusion framework for robust stock movement prediction under uncertainty}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {17494},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-43381-3},
url = {https://doi.org/10.1038/s41598-026-43381-3},
pmid = {41957129},
pmcid = {PMC13237071}
}

RIS

TY - JOUR
AU - Wang, Tian Wen
AU - Shaikh, Zaffar Ahmed
AU - Yong, Sook Lu
AU - Elmannai, Hela
AU - Por, Lip Yee
TI - FusionLSTM-CNF: a confidence-calibrated multi-modal late fusion framework for robust stock movement prediction under uncertainty
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/09
VL - 16
IS - 1
SP - 17494
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-43381-3
UR - https://doi.org/10.1038/s41598-026-43381-3
LA - en
ER -

CSL-JSON

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