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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.