Stock index trend prediction based on tabnet feature selection and long short-term memory

HIGHLIGHTS

• who: Xiaolu Wei and colleagues from the Business School, Hubei University, Wuhan, Hubei, China, Department of Economics, Huazhong have published the paper: Stock index trend prediction based on TabNet feature selection and long short-term memory, in the Journal: PLOS ONE of 15/Oct/2021
• what: The authors propose a predictive model TabLSTM that combines machine learning methods such as and Long Short-Term Memory Neural Network (LSTM) with a complete factor library for the motivation is the notion that there are numerous interrelated factors in the market and the factors that affect each are . . .
  

   

  

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