Kripto Para Fiyat Dinamiklerinin Tahmini: Makine Öğrenmesi Tekniklerinin Karşılaştırmalı Analizi

Abstract

Son yıllarda dijitalleşmenin hız kazanmasıyla birlikte finansal sistemlerde önemli dönüşümler yaşanmış, kripto paralar ise merkeziyetsiz yapıları, artan işlem hacimleri ve yatırım potansiyelleriyle dikkat çekmiştir. Ancak yüksek fiyat oynaklığı ve piyasa belirsizlikleri, kripto para fiyat tahminini zorlaştırmakta ve bu alanda geliştirilen modellerin stratejik önemini artırmaktadır. Bu çalışmada, saatlik ve günlük zaman çözünürlüklerinde 15 popüler kripto para biriminin fiyatları için Gradient Boosting Regression (GBR), Random Forest Regression (RFR), Ridge Regression (RR) ve Light Gradient Boosting Machine (LGBM) algoritmaları karşılaştırmalı olarak değerlendirilmiştir. Çalışma kapsamında iki farklı modelleme stratejisi benimsenmiştir: ilki, her bir kripto para için bağımsız biçimde eğitilen bireysel modelleri; ikincisi ise tüm varlıkların verilerinin birleştirildiği çok-varlıklı modelleri kapsamaktadır. Bireysel veri setlerinde RR algoritması kısa vadeli dalgalanmaları en yüksek doğrulukla tahmin etmiş (R² ≈ 0.998, MAE ≈ 0.007, RMSE ≈ 0.008) ve GBR ile RFR modelleri rekabetçi performans sergilemiştir. LGBM bazı varlıklarda kısa vadeli oynaklığa karşı daha yüksek duyarlılık göstermiştir. Çok-varlıklı veri setlerinde RFR, hem saatlik hem günlük verilerde en kararlı ve doğru tahminleri üretmiştir (MAE ≈ 0.038 saatlik, 0.165 günlük; RMSE ≈ 0.185 saatlik, 0.53 günlük). LGBM kısa vadeli verilerde güçlü performansını sürdürürken, GBR genel olarak istikrarlı kalmış ancak bazı varlıklarda saatlik RMSE değeri yüksek bulunmuştur; RR ise bazı günlük senaryolarda daha yüksek hata üretmiştir. Elde edilen sonuçlar, kripto para fiyat tahmininde başarının yalnızca kullanılan algoritmaya değil, aynı zamanda veri yapısına, modelleme yaklaşımına ve zaman çözünürlüğüne güçlü biçimde bağlı olduğunu; kısa vadeli fiyat hareketlerinin hem bireysel hem de çok-varlıklı veri setleriyle yüksek doğrulukla öngörülebilirken, uzun vadeli tahminlerde algoritma seçimi ve veri entegrasyonunun belirleyici bir rol oynadığını ortaya koymaktadır.

Keywords

• Kripto para fiyat tahmini
• zaman serisi tahmini
• volatilite modellemesi
• makine öğrenmesi
• regresyon

Predicting Cryptocurrency Price Dynamics: A Comparative Analysis of Machine Learning Techniques

Abstract

In recent years, the rapid acceleration of digitalization has led to significant transformations in financial systems, with cryptocurrencies drawing attention due to their decentralized structures, increasing transaction volumes, and investment potential. However, high price volatility and market uncertainties make cryptocurrency price prediction challenging, thereby increasing the strategic importance of developing effective predictive models in this field. In this study, the prices of 15 popular cryptocurrencies were comparatively evaluated using four regression algorithms—Gradient Boosting Regression (GBR), Random Forest Regression (RFR), Ridge Regression (RR), and Light Gradient Boosting Machine (LGBM)—at hourly and daily time resolutions. Two distinct modeling strategies were adopted: the first involved training independent models for each cryptocurrency, while the second combined all asset data into multivariate models. In the individual datasets, the RR algorithm achieved the highest accuracy in capturing short-term fluctuations (R² ≈ 0.998, MAE ≈ 0.007, RMSE ≈ 0.008), while GBR and RFR demonstrated competitive performance. The LGBM model exhibited higher sensitivity to short-term volatility for certain assets. In the multivariate datasets, RFR produced the most stable and accurate predictions for both hourly and daily data (MAE ≈ 0.038 hourly, 0.165 daily; RMSE ≈ 0.185 hourly, 0.53 daily). While LGBM maintained strong performance on short-term data and GBR remained generally consistent—albeit with higher hourly RMSE for some assets—RR produced larger errors in certain daily scenarios. The results indicate that the success of cryptocurrency price prediction depends not only on the chosen algorithm but also strongly on the data structure, modeling strategy, and temporal resolution; short-term price movements can be predicted with high accuracy using both individual and multivariate datasets, whereas in long-term forecasts, algorithm selection and data integration play a decisive role.

Keywords

• Cryptocurrency price prediction
• Time series forecasting
• Volatility modeling
• Machine learning
• Regression

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