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Journals / Uygulamalı Ekonomi ve Sosyal Bilimler Dergisi / 2026 / Cilt: 8 Sayı: 1

FORECASTING CRYPTOCURRENCY VOLUMES WITH ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR MACHINES

YAPAY SİNİR AĞLARI VE DESTEK VEKTÖR MAKİNELERİ İLE KRİPTO PARA HACİMLERİNİN TAHMİNİ

Haitham Nadhim Mohammed0009-0003-3117-9322
Yıldırım Demir(VAN YÜZÜNCÜ YIL ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ, EKONOMETRİ BÖLÜMÜ, İSTATİSTİK ANABİLİM DALI)0000-0002-6350-8122
Pages
27–46
Publication Date
2026-03-28
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Findings indicate that the ANN model provides more accurate predictions for cryptocurrency volumes.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, 50-day volume forecasts for Bitcoin, Ethereum, Binance Coin, and Ripple were conducted using Artificial Neural Networks and Support Vector Machines.
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Key Findings & Data:The root mean square error, mean absolute error, and error autocorrelation at lag 1 were used to compare model performance.
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Conclusion & Impact:Findings indicate that the ANN model provides more accurate predictions for cryptocurrency volumes.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kripto para · Sinir ağları · Destek vektör makineleri

Abstract

In this study, 50-day volume forecasts for Bitcoin, Ethereum, Binance Coin, and Ripple were conducted using Artificial Neural Networks and Support Vector Machines. The root mean square error, mean absolute error, and error autocorrelation at lag 1 were used to compare model performance. Analyses were carried out in R using data from investing.com. Findings indicate that the ANN model provides more accurate predictions for cryptocurrency volumes. According to 50-day forecasts, BTC volume is expected to increase, while BNB volume decreases with the ANN model but increases with the SVM model. For ETH and XRP, the ANN model indicates stable horizontal movements, whereas the SVM model predicts a sharp increase in ETH volume and a decline in XRP volume. Overall, although cryptocurrencies are innovative financial assets, their high volatility poses significant risks, suggesting they are not yet reliable investment instruments.

Özet

Bu çalışmada, Bitcoin, Ethereum, Binance Coin ve Ripple için 50 günlük hacim tahminleri Yapay Sinir Ağları (YSA) ve Destek Vektör Makineleri (SVM) kullanılarak gerçekleştirilmiştir. Model performansını karşılaştırmak için kök ortalama karekök hatası, ortalama mutlak hata ve gecikme 1’deki hata oto korelasyonu kullanılmıştır. Analizler investing.com’dan alınan veriler kullanılarak R’de gerçekleştirilmiştir. Bulgular, YSA modelinin kripto para hacimleri için daha doğru tahminler sağladığını göstermektedir. 50 günlük tahminlere göre BTC hacminin artması beklenirken, BNB hacmi YSA modeliyle azalırken SVM modeliyle artmaktadır. ETH ve XRP için YSA modeli istikrarlı yatay hareketler gösterirken, SVM modeli ETH hacminde keskin bir artış ve XRP hacminde bir düşüş öngörmektedir. Genel olarak, kripto paralar yenilikçi finansal varlıklar olsa da yüksek oynaklıkları önemli riskler oluşturmakta ve henüz güvenilir yatırım araçları olmadıklarını göstermektedir.

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Cite This Article

Mohammed, H. N., Demir, Y. (2026). FORECASTING CRYPTOCURRENCY VOLUMES WITH ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR MACHINES. Uygulamalı Ekonomi ve Sosyal Bilimler Dergisi, Cilt: 8 Sayı: 1, 27–46. https://doi.org/10.46959/jeess.1803960

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