EVALUATING THE PERFORMANCE OF BITCOIN PRICE FORECASTING USING MACHINE LEARNING TECHNIQUES ON HISTORICAL DATA

Od momentu wejścia na rynek w 2009 roku, cena Bitcoina jest niezwykle nieregularna. Na jego cenę wpływają takie czynniki, jak wskaźniki popularności, zmiany regulacyjne, wydarzenia geopolityczne i zmiany makroekonomiczne. Eksperci uważają, że cena Bitcoina wzrośnie w dłuższej perspektywie ze względu na ograniczoną podaż i rosnący popyt. Dlatego też celem niniejszego badania jest zaproponowanie podejścia opartego na selekcji cech i uczeniu maszynowym do przewidywania ceny bitcoina. Do tego celu badawczego wykorzystano, zwizualizowano i wstępnie przetworzono zbiór danych oparty na kryptowalutach. Zastosowano pięć różnych podejść do wyboru cech (Pearson, RFE, Embedded Random Forest, Tree-based i Light GBM), a następnie metodologię ensemble, z podejściem maksymalnego głosowania w celu wyodrębnienia najważniejszych cech i wygenerowania zbioru danych ze zredukowanymi atrybutami. Następnie zbiór danych z lub bez selekcji cech jest wykorzystywany do przewidywania cen bitcoinów poprzez zastosowanie dziesięciu różnych modeli regresji uczenia maszynowego, w tym sześciu tradycyjnych, czterech technik baggingu i boostingu. Analiza porównawcza wyników za pomocą wielu parametrów wydajności pokazuje, że zmniejszona liczba cech poprawia wydajność każdego z modeli, a modele zespołowe przewyższają inne typy modeli. W związku z tym model Random Forest regression ensemble ML może uzyskać najlepszą dokładność przewidywania z 0,036018 RMSE, 0,029470 MAE i 0,934512 R2, wykorzystując zbiór danych ze zredukowanymi funkcjami do szacowania wartości bitcoinów.

Słowa kluczowe:

uczenie maszynowe, bitcoin, modele regresji, selekcja cech zespołu

Bibliografia

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OCENA SKUTECZNOŚCI PROGNOZOWANIA CEN BITCOINÓW PRZY UŻYCIU TECHNIK UCZENIA MASZYNOWEGO NA DANYCH HISTORYCZNYCH. (2024). Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 14(2), 101-108. https://doi.org/10.35784/iapgos.5657

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Numer Tom 14 Nr 2 (2024)

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