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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Vydáno v:	PloS one Ročník 19; číslo 8; s. e0308488
Hlavní autoři:	Tang, Zhenyang, Huang, Jinshui, Rinprasertmeechai, Denisa
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 08.08.2024 Public Library of Science (PLoS)
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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 fixed time window of historical information, such as RNN and LSTM models. However, these models only consider the short-term and point-to-point relationships within stock series. The financial market is a complex and dynamic system with many unobservable temporal patterns. Therefore, we propose an adaptive period-aggregation model called the Latent Period-Aggregated Stock Transformer (LPAST). The model integrates a variational autoencoder (VAE) with a period-to-period attention mechanism for multistep prediction in the financial time series. Additionally, we introduce a self-correlation learning method and routing mechanism to handle complex multi-period aggregations and information distribution. Main contributions include proposing a novel period-aggregation representation scheme, introducing a new attention mechanism, and validating the model’s superiority in long-horizon prediction tasks. The LPAST model demonstrates its potential and effectiveness in financial market prediction, highlighting its relevance in financial research and predictive analytics.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: NO authors have competing interests.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0308488