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

Author

Listed:
  • Zhenyang Tang
  • Jinshui Huang
  • Denisa Rinprasertmeechai

Abstract

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.

Suggested Citation

  • Zhenyang Tang & Jinshui Huang & Denisa Rinprasertmeechai, 2024. "Period-aggregated transformer for learning latent seasonalities in long-horizon financial time series," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-22, August.
  • Handle: RePEc:plo:pone00:0308488
    DOI: 10.1371/journal.pone.0308488
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    References listed on IDEAS

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