Comparison Between ARIMA and LSTMRNN for VNIndex Prediction. The VNIndex prediction problem is a part of a research project that predicts macroeconomic indicators for developing economies to detect the risk of inflation and economic crisis. In this study, given the records of VNIndex in the Vietnam Stock Exchange (VSE), we compare two different VNIndex forecast methods: the ARIMA econometric model and the Deeplearning approach with the LSTMRNN model. Empirical results shows that: (1) the LSTMRNN model is more accurate than the ARIMA model in the VNIndex forecast problems, (2) when applying the LSTMRNN model, the window size would converge if it was larger than 15 days for LSTM256 and 10 days for LSTM512. From such results, we conclude that the LSTMRNN model based on onemonth data is optimal, which is appropriate according to the Wave theory and the psychology of the market.

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.