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

Abstract

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