Akademik Veri Yönetim Sistemi
Akıllı Sistemlerde Yenilikler ve Uygulamaları Konferansı (ASYU), Sivas, Türkiye, 3 - 05 Ekim 2023, ss.1-5
The accurate prediction of an uncertain future is crucial for society. Particularly when it comes to finance, where it holds vital importance for companies. Time series analysis is conducted to forecast the potential value of financial assets in the near future. Due to the impracticality of manually analyzing hundreds of indicators, machine learning methods are frequently employed. This study showcases the optimal value of the lag hyperparameter used in Random Forest and Extreme Gradient Boosting methods. The daily Bitcoin OHLCV data of a time series was enriched with indicators, and it was observed that the lag hyperparameter yielded the best results when considering the preceding 23 time series units.