2026-05-15T12:26:11.011Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:9707 2024-12-03T20:10:47Z 1

Multivariate Bitcoin price prediction based on LSTM tuned by hybrid reptile search algorithm 2023 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/9707 https://ieeexplore.ieee.org/abstract/document/10316108 M. Todorovic A. Petrovic A. Toskovic M. Zivkovic L. Jovanovic N. Bacanin This study focuses on analyzing historical data to forecast future trends in Bitcoin prices due to its influence on the business landscape. Its high volatility attracted attention to understanding the influencing factors for its price. This paper presents an empirical investigation using time-series data of various exogenous and endogenous variables. Closing prices of Bitcoin and Ethereum, along with the daily volume of Bitcoin-related tweets are examined for Bitcoin closing price prediction by a long-short term memory (LSTM) network, fine-tuned by a hybrid adaptive reptile search algorithm. The analysis covers a three-year period, in which data is divided into training, validation, and testing sets. Comparative analysis against LSTM networks tuned by other high-performing metaheuristic algorithms demonstrates that the novel approach outperforms competitors in terms of standard regression metrics. conferenceObject 195 198 10.1109/TELSIKS57806.2023.10316108 2023 16th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), IEEE, Nis, Serbia