An End-to-End Structure with Novel Position Mechanism and Improved EMD
  for Stock Forecasting
Chufeng Li Jianyong Chen
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 which
both stock market information and individual stock information are considered.
Meanwhile, we propose a novel EMD-based algorithm for reducing short-term noise
in stock data. Two randomly selected exchange-traded funds (ETFs) spanning over
ten years from US stock markets are used to demonstrate the superior
performance of the proposed attention-based method. The experimental analysis
demonstrates that the proposed attention-based method significantly outperforms
other state-of-the-art baselines. Code is available at
https://github.com/DurandalLee/ACEFormer.