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

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 multi

SUBMITTER: Tang Z 

PROVIDER: S-EPMC11309408 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature