• Corpus ID: 236493298

Feature importance recap and stacking models for forex price prediction

@inproceedings{Li2021FeatureIR,
  title={Feature importance recap and stacking models for forex price prediction},
  author={Yunze Li and Yanan Xie and Chen Yu and Fangxing Yu and Bor-Wei Jiang and Matloob Khushi},
  year={2021}
}
Forex trading is the largest market in terms of qutantitative trading. Traditionally, traders refer to technical analysis based on the historical data to make decisions and trade. With the development of artificial intelligent, deep learning plays a more and more important role in forex forecasting. How to use deep learning models to predict future price is the primary purpose of most researchers. Such prediction not only helps investors and traders make decisions, but also can be used for auto… 
1 Citations

Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis

TLDR
The TLS-LSTM algorithm is presented to improve the accuracy of Forex trend prediction of Australian Dollar and United States Dollar (AUD/USD) and outperforms Single-Layer Long Short-Term Memory, MLP, and CEEMDAN-IFALSTM.

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