Netflix Stock Price Trend Prediction Using Recurrent Neural Network

Abstract— Stocks are investments that have dynamic movements. Stock price changes move every day even hourly. With very fast changes, stock prices require predictions to be able determine market projections. Predictions used reduce risk when making transactions. In this study, of trends were made using the Recurrent Neural Network (RNN). The approach taken is perform a time series analysis RNN variance, namely Long Short Term Memory (LSTM). Hyperparameter construction in LSTM model testing simulation can estimate with maximum percentage accuracy. results showed prediction produced loss function 0.0012 and training 73 m/step. evaluation was carried out RMSE which resulted score 17.13325. obtained after doing machine learning 1239 data. models calculated by changing number epochs, variation between predicted current price. Computations dataset includes open, high, low, close, adj prices, closes, volumes. main objective study extent algorithm anticipates better Code seen at iranihoeronis/RNN-LSTM (github.com) Keywords— Prediction, Time Series, (RNN),