An End-to-End Structure with Novel Position Mechanism and Improved EMD for Stock Forecasting

arXiv:2404.07969v1 Announce Type: cross
Abstract: As a branch of time series forecasting, stock movement forecasting is one of the challenging problems for investors and researchers. Since Transformer was introduced to analyze financial data, many researchers have dedicated themselves to forecasting stock movement using Transformer or attention mechanisms. However, existing research mostly focuses on individual stock information but ignores stock market information and high noise in stock data. In this paper, we propose a novel method using the attention mechanism in …

arxiv cs.ai cs.lg forecasting novel q-fin.st stock type