Analysis of Price Volatility in BIST 100 Index With Time Series: Comparison of Fbprophet and LSTM Model

Making predictions about the future based on past datasets is one of most important issues in analytical finance. Recently developed deep learning approaches and machine models have increased interest this field. One these approaches, time series, trying to predict changes a certain frequency. In study, LSTM (Long Short-Term Memory) Fbprophet (Facebook Prophet) methods were used estimate data BIST-100 index. Predicting stock market indices with erratic behavior complex task, but new algorithms developed, price can become more predictable. The research was carried out index between 2021-01-01 2021-12-31, which has high volatility. evaluation criteria we are MAE (mean absolute error), MSE square error) RMSE (root mean error). As result it determined that model successful than low error rates.