2026-05-13T16:14:59.740Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

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Multivariate Bitcoin Price Prediction Based on Tuned Bidirectional Long Short-Term Memory Network and Enhanced Reptile Search Algorithm, Chapter in CCIS Communications in Computer and Information Science: ICIST 2023: International Conference on Information and Software Technologies, Springer, volume 1979 2024 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/9747 https://link.springer.com/chapter/10.1007/978-3-031-48981-5_4 I. Strumberger M. Zivkovic V. R. R. Thumiki A. Djordjevic J. Gajic N. Bacanin Cryptocurrency price prediction and investment is a popular and relevant area of business nowadays. It involves analyzing historical data to forecast future trends and movements in asset prices. Bitcoin has gained significant prominence in the worldwide financial market as an investment asset. However, the high volatility of its price has attracted considerable attention from researchers and investors alike, leading to a growing interest in understanding the factors that drive its movement. This paper builds upon a research and conducts an empirical approach into the time-series data of a diverse range of exogenous and endogenous variables. Specifically, in this paper, the closing prices of Bitcoin, Ethereum and the daily volume of Bitcoin-related tweets are examined. For forecasting closing Bitcoin price based on the above mentioned predictors, bidirectional long-short term memory (BiLSTM) network tuned by hybrid adaptive reptile search algorithm is proposed. The analysis covers a three-year period from January 2020 to August 2022 and employs a three-fold split of the data to train, validation, and testing datasets. The best generated model by algorithm introduced in this manuscript is compared to other BiLSTM networks tuned by other cutting-edge metaheuristics and achieved results revealed that the method introduced in this research outperformed all other competitors regarding standard regression metrics. bookPart Springer, Cham 38 52 10.1007/978-3-031-48981-5_4 CCIS Communications in Computer and Information Science: ICIST 2023: International Conference on Information and Software Technologies, volume 1979