Combine Facebook Prophet and LSTM with BPNN Forecasting financial markets : the Morgan Taiwan Index

被引：0

作者：

Fang, Wen-Xiang ^{[1
]}

Lan, Po-Chao ^{[1
]}

Lin, Wan-Rung ^{[1
]}

Chang, Hsiao-Chen ^{[1
]}

Chang, Hai-Yen ^{[1
]}

Wang, Yi-Hsien ^{[1
]}

机构：

[1] Chinese Culture Univ, Dept Banking & Finance, Taipei, Taiwan

来源：

2019 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ISPACS) | 2019年

关键词：

Deep learning; Time series; Forecast; MSCI Taiwan Index Futures; Financial market;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The recurrent neural network (RNN) used by many people in machine learning often faces the situation where the gradient disappears, in order to solve this problem. Modern scholars often use Long Short-Term Memory (LSTM) proposed in 1997 to predict time series samples. However, Facebook believes that most of the past time series models have missing adjustment parameters. Therefore, it developed a set of predictive tools Prophet for periodic parameters and trend parameters in 2017. In this paper, LSTM and Prophet are used to predict the trend of time series data, and the prediction trend is combined with the inverse neural network model (BPNN) for prediction. The empirical results show that this method can indeed achieve accurate forecasting trends and reduce errors. This research promises to contribute to this research literature in the future, thereby enhancing the ability of investors to target the long-term layout.

引用

页数：2