Period-aggregated transformer for learning latent seasonalities in long-horizon financial time series

Fluctuations in the financial market are influenced by various driving forces and numerous factors. Traditional financial research aims to identify the factors influencing stock prices, and existing works construct a common neural network learning framework that learns temporal dependency using a fi...

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Veröffentlicht in:	PloS one Jg. 19; H. 8; S. e0308488
Hauptverfasser:	Tang, Zhenyang, Huang, Jinshui, Rinprasertmeechai, Denisa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 08.08.2024 Public Library of Science (PLoS)
Schlagworte:	Algorithms Arbitrage Computational linguistics Computer and Information Sciences Decomposition Deep learning Electric transformers Engineering and Technology Financial Management Financial markets Forecasts and trends Humans Information management Investments - economics Language processing Latent period Learning Machine learning Models, Economic Natural language interfaces Natural language processing Neural networks Neural Networks, Computer Physical Sciences Predictions Principal components analysis Research and Analysis Methods Seasonal affective disorder Seasonal variations Seasons Securities markets Signal to noise ratio Social Sciences Stock prices Task complexity Time dependence Time Factors Time series Transformers Trends Windows (intervals)
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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