2026-05-10T08:03:23.152Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

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Tuned Long Short-Term Memory Model for Ethereum Price Forecasting Through an Arithmetic Optimization Algorithm, Chapter in LNNS Lecture Notes in Networks and Systems: IBICA 2022: International Conference on Innovations in Bio-Inspired Computing and Applications, Springer, volume 649 2023 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/9359 https://link.springer.com/chapter/10.1007/978-3-031-27499-2_31 M. Stankovic L. Jovanovic N. Bacanin M. Zivkovic M. Antonijevic P. Bisevac Machine learning as a subset of artificial intelligence presents a promising set of algorithms with an ability to gather experience and learn from provided data. This coupled with the expanding availability of computational resources and information transparency has made it possible to utilize algorithms to forecast prices. In recent years, cryptocurrency has increased in popularity and has seen wider adoption as a payment method. However, due to the volatile nature of the cryptocurrency market, casting accurate predictions can be quite challenging. One promising approach is the application of long-short-term memory artificial neural networks to time-series price data to attain results. The forecasting accuracy of machine learning models is highly dependent on adequate hyperparameter settings. Thus, this work, an improved version of the arithmetic optimization algorithm, is tasked with selecting optimal values of a long-short term network casting price predictions. The proposed approach has been tested on publicly available real-world Ethereum trading price data and according to the results of comparative analysis with other contemporary metaheuristics, it has been concluded that the proposed method achieved excellent results, and outperformed aforementioned algorithms in one and four-step ahead predictions. bookPart Springer, Cham 327 337 10.1007/978-3-031-27499-2_31 LNNS Lecture Notes in Networks and Systems: IBICA 2022: International Conference on Innovations in Bio-Inspired Computing and Applications, volume 649