The PSR-Transformer Nexus: A Deep Dive into Stock Time Series Forecasting
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10256317" target="_blank" >RIV/61989100:27240/23:10256317 - isvavai.cz</a>
Result on the web
<a href="https://thesai.org/Publications/ViewPaper?Volume=14&Issue=12&Code=IJACSA&SerialNo=92" target="_blank" >https://thesai.org/Publications/ViewPaper?Volume=14&Issue=12&Code=IJACSA&SerialNo=92</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14569/IJACSA.2023.0141292" target="_blank" >10.14569/IJACSA.2023.0141292</a>
Alternative languages
Result language
angličtina
Original language name
The PSR-Transformer Nexus: A Deep Dive into Stock Time Series Forecasting
Original language description
Accurate stock market forecasting has remained an elusive endeavor due to the inherent complexity of financial systems dynamics. While deep neural networks have shown initial promise, robustness concerns around long-term dependencies persist. This research pioneers a synergistic fusion of nonlinear time series analysis and algorithmic advances in representation learning to enhance predictive modeling. Phase space reconstruction provides a principled way to reconstruct multidimensional phase spaces from single variable measurements, elucidating dynamical evolution. Transformer networks with self -attention have recently propelled state-of-the-art results in sequence modeling tasks. This paper introduces PSR-Transformer Networks specifically tailored for stock forecasting by feeding PSR interpreted constructs to transformer encoders. Extensive empirical evaluation on 20 years of historical equities data demonstrates significant accuracy improvements along with enhanced robustness against LSTM, CNNLSTM and Transformer models. The proposed interdisciplinary fusion establishes new performance benchmarks on modeling financial time series, validating synergies between domain -specific reconstruction and cutting -edge deep learning.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International journal of advanced computer science and applications
ISSN
2158-107X
e-ISSN
2156-5570
Volume of the periodical
14
Issue of the periodical within the volume
12
Country of publishing house
US - UNITED STATES
Number of pages
8
Pages from-to
917-924
UT code for WoS article
001244472600021
EID of the result in the Scopus database
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