Data Driven Artificial Neural Network LSTM Hybrid Predictive Model Applied for International Stock Index Prediction

In this paper, a neural network long short term memory hybrid model focusing on stock index forecasting is modeled, presented, and investigated. This model is tuned by artificial intelligence, and neural networks in Python environment. Accordingly, it can perform the prediction with a high accuracy, and near to the real value. Four layers of input layer, hidden layer, attention layer, as well as the output layer set up the proposed hybrid model. The input data, and API-based server connection are performed in input layer. The hidden layer performs the calculations, and measurements. Price value forecasting, and prediction-train graphs done by the attention layer, and output layer, respectively. The proposed system conceptualized the effect of artificial intelligence (AI), and machine learning (ML) on financial markets. Finally, it is concluded this model can be utilized in other wide range of financial applications.