Forecasting bitcoin: decomposition aided long short-term memory based time series and its with values

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorMizdrakovic, Vule
dc.contributor.authorKljajic, Maja
dc.contributor.authorZivkovic, Miodrag
dc.contributor.authorBacanin, Nebojsa
dc.contributor.authorJovanovic, Luka
dc.date.accessioned2025-04-18T10:04:14Z
dc.date.available2025-04-18T10:04:14Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractBitcoin price volatility fascinates both researchers and investors, studying features that influence its movement. This paper expends on previous research and examines time series data of various exogenous and endogenous factors: Bitcoin, Ethereum, S&P 500, and VIX closing prices; exchange rates of the Euro and GPB to USD; and the number of Bitcoin-related tweets per day. A period of three years (from September 2019 to September 2022) is covered by the research dataset. A two -layer framework is introduced tasked with accurately forecasting Bitcoin price. In the first layer, to account for complexities in the analyzed data, variational mode decomposition (VMD) extracts trends from the time series. In the second layer, Long short-term memory and hybrid Bidirectional long short-term memory networks were used to forecast prices several steps ahead. This work also introduced an enhanced variant of the sine cosine algorithm to tune the control parameters of VMD and both neural networks for attaining the best possible performance. The main focus is on combining VMD with modified metaheuristics to improve cryptocurrency closing value forecast. Two sets of experiments were conducted, with and without VMD. The results have been contrasted with models tuned by seven other cuttingedge optimizers. Extensive experimental outcomes indicate that Bitcoin price can be forecasted with great accuracy using selected features and time series decomposition. Additionally, the best model was analyzed, and Shapley values indicated that features such as EUR/USD exchange rates, Ethereum closing prices, and GBP/USD exchange rates, have a significant impact on forecasts.
dc.description.sponsorshipScience Fund of the Republic of Serbia
dc.identifier.citationMizdrakovic, V., Kljajic, M., Zivkovic, M., Bacanin, N., Jovanovic, L., Deveci, M., & Pedrycz, W. (2024). Forecasting bitcoin: Decomposition aided long short-term memory based time series modelling and its explanation with shapley values. Knowledge-Based Systems, 112026.
dc.identifier.doi10.1016/j.knosys.2024.112026
dc.identifier.endpage22
dc.identifier.issn0950-7051
dc.identifier.issn1872-7409
dc.identifier.scopus2-s2.0-85195578850
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.knosys.2024.112026
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6934
dc.identifier.volume299
dc.identifier.wosWOS:001253644600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofKnowledge management system
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectInvestor Sentiment
dc.subjectVariational Mode Decomposition
dc.subjectBidirectional Long Short-Term Memory
dc.subjectMetaheuristics Optimization
dc.subjectSine Cosine Algorithm
dc.titleForecasting bitcoin: decomposition aided long short-term memory based time series and its with values
dc.typeArticle

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