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Journals / Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Sayı: 73

The deep learning revolution in cryptocurrency markets: An interdisciplinary modeling approach with VAE-GAN

Kripto para piyasalarında derin öğrenme devrimi: VAE-GAN ile disiplinlerarası bir modelleme yaklaşımı

Figen Aldı(Pamukkale Üniversitesi)0000-0002-8583-1705
İlhan Küçükkaplan(PAMUKKALE ÜNİVERSİTESİ)0000-0001-6926-3659
Pages
13–25
Publication Date
2026-04-30
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AI Summary & Key Takeaways

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

The findings indicate that artificial intelligence-based modeling techniques represents a powerful method to cryptocurrency price forecasting and by successfully capturing market dynamics and proves to be an effective tool in prediction future price movements.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study examines the fundamental factors determining cryptocurrency price movements, the feasibility of short- and long-term price predictions, and the variables influencing these forecasts.
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Key Findings & Data:In the study, an approach that integrates different artificial intelligence techniques was adopted to optimize price predictions in cryptocurrency markets.
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Conclusion & Impact:The findings indicate that artificial intelligence-based modeling techniques represents a powerful method to cryptocurrency price forecasting and by successfully capturing market dynamics and proves to be an effective tool in prediction future price movements.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kripto para · dijital dönüşüm · yapay zekâ

Abstract

This study examines the fundamental factors determining cryptocurrency price movements, the feasibility of short- and long-term price predictions, and the variables influencing these forecasts. Additionally, it aims to identify the most suitable artificial intelligence method for achieving the most reliable predictions with minimal error. In this context, a model was developed to determine the closest prediction range to reality by including deep neural networks, and the findings were intended to guide investors and market makers. In the study, an approach that integrates different artificial intelligence techniques was adopted to optimize price predictions in cryptocurrency markets. The stationarity properties of the time series data were examined, and the ADF, PP, and KPSS tests were employed to assess their impact on forecast accuracy. Forecasts conducted using the VAE-GAN model demonstrated that training the model with stationary data allowed for better adaptation to volatile market conditions. Conversely, analyses performed with non-stationary data revealed that higher accuracy rates were achieved for crypto assets with high market capitalization and trading volume. The findings indicate that artificial intelligence-based modeling techniques represents a powerful method to cryptocurrency price forecasting and by successfully capturing market dynamics and proves to be an effective tool in prediction future price movements.

Özet

Bu çalışma, kripto para fiyat hareketlerini belirleyen temel faktörleri, kısa ve uzun vadeli fiyat tahminlerinin uygulanabilirliğini ve bu tahminlerin hangi değişkenlerden etkilendiği incelemektedir. Ek olarak, minimum hata ile en güvenilir tahminleri elde etmek için en uygun yapay zekâ yöntemini belirlemeyi hedeflemiştir. Bu bağlamda, derin sinir ağları da dâhil edilerek, gerçeğe en yakın tahmin aralığını tespit etmeye yönelik bir model geliştirilmiş ve elde edilen bulguların yatırımcılar ile piyasa yapıcılar için yol gösterici nitelikte olması amaçlanmıştır. Çalışmada, kripto para piyasalarındaki fiyat tahminlerini optimize etmek amacıyla farklı yapay zekâ tekniklerini bütünleştiren bir yaklaşım benimsenmiştir. Zaman serisi verilerinin durağanlık özellikleri incelenmiş ve tahmin doğruluğu üzerindeki etkilerini değerlendirmek amacıyla ADF, PP ve KPSS testlerinden yararlanılmıştır. VAE-GAN modeli ile gerçekleştirilen tahminler, modelin durağanlaştırılmış verilerle eğitildiğinde değişken piyasa koşullarına daha iyi adapte olduğunu göstermiştir. Öte yandan, durağanlaştırılmamış verilerle yapılan analizler, yüksek piyasa değeri ve işlem hacmine sahip kripto varlıklar için daha yüksek doğruluk oranları elde edildiğini ortaya koymuştur. Bulgular, yapay zekâ tabanlı modelleme tekniklerinin kripto para fiyat tahmininde güçlü bir yöntem sunduğunu ve piyasa dinamiklerini başarılı bir şekilde yakalayarak gelecekteki fiyat hareketlerini öngörmede etkin bir araç olduğunu göstermektedir.

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Cite This Article

Aldı, F., Küçükkaplan, İ. (2026). The deep learning revolution in cryptocurrency markets: An interdisciplinary modeling approach with VAE-GAN. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Sayı: 73, 13–25. https://doi.org/10.18070/erciyesiibd.1692272

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