Stock Market Forecasting Based on Long-Short Term Memory Model

Many authors have been utilizing various data models, machine learning, and data mining to anticipate the future movement of stock prices since the stock market's beginnings. This study uses stacked LSTM deep learning to establish a prediction model with view to forecasting Netflix stock values on day-closing. The "Stock ticker" characteristic is used as an input in the forecasting model, which forecasts stock market closing price as a chart using a web application written in Python. Date, Open, Close, High, and Low are the attributes that are included in the model. Data was gathered between the years 2019 and 2022, and I separated it into two parts: a training set and a testing set. Only the testing portion is to be used for the final forecast. The closing time is then displayed against time on a graph. The results suggest that NETFLIX functions effectively.