Comparison of Back Propagation, Long Short-Term Memory (LSTM), Attention-Based LSTM Neural Networks Application in Futures Market of China using R Programming

Wang Shuangao, Liu Yi, Rajchandar Padmanaban, Mohamed Shamsudeen, Subalakshmi R


Artificial neural network is widely used in the financial time series, but Long short-term memory (LSTM) neural network is rarely used in the futures market in China. In this paper, the LSTM neural network is studied by using futures data. The daily trading data of four groups of futures such as silver, copper, lithium and coking coal from December 2014 to December 2018 are used as the training object to make short-term prediction of the closing price. By comparing the Back Propagation (BP) neural network, general multi-layer LSTM neural network, and using the attention mechanism optimization LSTM contrast test, the result of the experiment shows that the futures price trend forecast time sequence, attention mechanism to promote significant effect of time sequence, and LSTM combined effect, by adjusting the parameters setting, using the improved LSTM neural network for time series prediction accuracy is higher, better generalization ability.


LSTM Neural Network; Futures Forecasting; Attention Mechanism; Financial Engineering

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