Comparing Forecasting Powers Of Traditional Methods And Learning Based Methods In Cryptocurrency Market: An Application On Bitcoin, Ethereum, Binance Coin And Monero

Tahsin Galip Tekin; Sait Patır

doi:10.52791/aksarayiibd.1770977

EN TR

Comparing Forecasting Powers Of Traditional Methods And Learning Based Methods In Cryptocurrency Market: An Application On Bitcoin, Ethereum, Binance Coin And Monero

Abstract

In this study, it is aimed to compare quantitative forecasting methods (traditional and learning based) in cryptocurrency market. For his purpose the daily prices between 16 September 2017 – 15 September 2022 of Bitcoin, Ethereum, Binance Coin and Monero were analyzed with five different methods: ARIMA, exponential smoothing, artificial neural networks, RNN and LSTM. In the results it is indicated that exponential smoothing method is the most successful method at forecasting daily prices. The method has high performance in forecasting BTC, ETH and BNB daily prices. But at forecasting daily XMR prices, artificial neural networks method was the most successful one. The other point which was detected in this study is deep learning based methods made some unsuccessful forecasts. This is thought to be due to the fact that deep learning methods require more data. In future studies, using other quantitative methods (e.g. GRU, XGBoost, transformer models) on other cryptocurrencies will contribute to the literature.

Keywords

Geleneksel Yöntemlerin Ve Öğrenme Temelli Yöntemlerin Tahmin Güçlerinin Kripto Para Piyasasında Karşılaştırılması: Bitcoin, Ethereum, Binance Coin Ve Monero Üzerine Bir Uygulama

Öz

Bu çalışmada nicel tahmin yöntemlerinin (geleneksel ve öğrenme tabanlı) tahmin güçlerinin kripto para piyasasında karşılaştırılması amaçlanmıştır. Bu amaca yönelik olarak Bitcoin, Ethereum, Binance Coin ve Monero’nun 16 Eylül 2017 – 15 Eylül 2022 aralığındaki günlük fiyatları beş farklı yöntemle analiz edilmiştir: ARIMA, üstel düzeltme, yapay sinir ağları, RNN ve LSTM. Sonuçlarda günlük fiyat tahminlemesinde en başarılı yöntemin üstel düzeltme yöntemi olduğu görülmüştür. Yöntem, BTC, ETH ve BNB günlük fiyat tahminlemesinde yüksek performansa sahiptir. Fakat XMR günlük fiyat tahminlemesinde yapay sinir ağları en başarılı olan yöntemdir. Çalışmada tespit edilen diğer bir husus derin öğrenme tabanlı yöntemlerin bazı başarısız tahminlemeler yapmasıdır. Bu durumun derin öğrenme yöntemlerinin daha fazla veriye ihtiyaç duymasından kaynaklandığı düşünülmektedir. Gelecek çalışmalarda, başka nicel yöntemlerin (Örnek: GRU, XGBoost, transformer modeller) başka kripto para birimleri üzerinde kullanılması literature katkı sağlayacaktır.

Anahtar Kelimeler

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Ayrıntılar

Birincil Dil

İngilizce

Konular

İşletme

Bölüm

Araştırma Makalesi

Yazarlar

Tahsin Galip Tekin ^*
0000-0002-2642-5838
Türkiye

Sait Patır
0000-0002-1592-1094
Türkiye

Yayımlanma Tarihi

26 Mart 2026

Gönderilme Tarihi

23 Ağustos 2025

Kabul Tarihi

17 Şubat 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 18 Sayı: 1

DOI

https://doi.org/10.52791/aksarayiibd.1770977

IZ

https://izlik.org/JA54CH99SK

Kaynak Göster

RIS / Bibtex

APA

Tekin, T. G., & Patır, S. (2026). Comparing Forecasting Powers Of Traditional Methods And Learning Based Methods In Cryptocurrency Market: An Application On Bitcoin, Ethereum, Binance Coin And Monero. Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 18(1), 37-50. https://doi.org/10.52791/aksarayiibd.1770977