Combine Facebook Prophet and LSTM with BPNN Forecasting financial markets: the Morgan Taiwan Index 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 ShortTerm 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 longterm layout.

The Financial Data Prediction System Based on the Recurrent Neural Network This paper introduces the breakthrough achievements of the artificial intelligence in recent years, designs the system on the basis of RNN related technology, and then demonstrates the predictive effect of the system through the experimental group, and evaluates the predictive accuracy of the system by using the deep learning related evaluation algorithm. The experimental evaluation results show that the scheme of using the recurrent neural network to learn and analyze historical data and using its model to predict the future trend of the financial data has high reliability and accuracy. Therefore, the indepth learning has great potential in the field of the financial forecasting.