A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model

【Author】 Zhang, Xinchen; Zhang, Linghao; Zhou, Qincheng; Jin, Xu

【Source】COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE

【影响因子】3.120

【Abstract】As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency trades, among other applications today. Using the historical price series of Bitcoin and gold from 9/11/2016 to 9/10/2021, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research. We first employ a noise reduction approach based on the wavelet transform to smooth the fluctuations of the price data, which has been shown to increase the accuracy of subsequent predictions. Second, we apply a wavelet transform to diminish the influence of high-frequency noise components on prices. Third, in the price prediction model, we develop an optimized LSTM prediction model (LSPM-P) and train it using historical price data for gold and Bitcoin to make accurate predictions. As a consequence of our model, we have a high degree of accuracy when projecting future pricing. In addition, our LSTM-P model outperforms both the conventional LSTM models and other time series forecasting models in terms of accuracy and precision.

【Keywords】

【发表时间】2022 MAY 5

【收录时间】2022-06-08

【文献类型】实证性文章

【主题类别】

区块链应用-虚拟经济-金融领域