Integrating Seasonal Trends and Market Volatility: A Hybrid SARIMA-XGBoost Model for Cryptocurrency Forecasting

Abstract

This paper explores the application of a hybrid SARIMA-XGBoost model for forecasting cryptocurrency prices within the context of financial time series analysis. Due to their dynamic nature, high volatility, seasonal patterns, and asymmetric market behavior, cryptocurrencies pose significant challenges for traditional forecasting methods. The study combines the strength of the SARIMA model in capturing seasonal and trend components with the XGBoost algorithm's ability to model nonlinear relationships. This hybrid approach aims to better represent the complex and non-stationary nature of cryptocurrency markets, thereby improving forecasting accuracy. Cryptocurrencies, such as Bitcoin, exhibit volatile price movements influenced by factors like periodic "halving" effects and volatility clustering. Additionally, investor behaviors, herd psychology, and social media sentiment contribute to the difficulty of price forecasting. In this study, residuals from the SARIMA model are used as inputs for the XGBoost algorithm to capture nonlinear patterns, effectively integrating both linear and nonlinear elements in a single model. The paper analyzes the model's performance using BTC (Bitcoin), XRP (Ripple), and ETH (Ethereum) price data. Metrics such as the Akaike Information Criterion (AIC), Mean Squared Error (MSE), and Mean Absolute Error (MAE) are employed to evaluate the model's accuracy. Results indicate that the SARIMA-XGBoost hybrid model outperforms standalone SARIMA and XGBoost models, achieving higher accuracy, especially during periods of market volatility. The hybrid model successfully captures the dynamic nature of cryptocurrency markets, producing lower error rates compared to other models. The findings demonstrate the effectiveness of hybrid modeling approaches in forecasting cryptocurrency prices and highlight the potential of optimized hybrid models in investment decision-making and risk management. However, the study also notes that the model's performance could be further enhanced during short-term fluctuations and highly volatile market conditions.

Keywords

• SARIMA
• XGBoost
• Cryptocurrency Forecasting
• Financial Time Series
• Volatility
• Hybrit Modeling

Mevsimsel Trendler ve Piyasa Volatilitesinin Entegrasyonu: Kripto Para Tahmini için Hibrit SARIMA-XGBoost Modeli

Abstract

Bu makale, finansal zaman serisi analizi bağlamında, kripto para birimlerinin fiyat tahmini için hibrit bir model olan SARIMA-XGBoost yöntemini ele almaktadır. Kripto para birimlerinin dinamik yapısı, yüksek volatilite, mevsimsel desenler ve asimetrik piyasa davranışları nedeniyle geleneksel yöntemlerle analiz edilmesi oldukça zordur. Makalede, SARIMA modelinin mevsimsel ve trend bileşenlerini ele almadaki gücü ile XGBoost algoritmasının doğrusal olmayan ilişkileri modelleme yeteneği birleştirilmiştir. Bu hibrit model, kripto para piyasasının karmaşık ve durağan olmayan yapısını daha iyi temsil ederek tahmin performansını artırmayı amaçlamaktadır. Kripto varlıklar, Bitcoin gibi önde gelen para birimlerinin dönemsel “yarılanma” etkileri ve volatilite kümeleşmesi gibi faktörler nedeniyle dalgalı fiyat hareketleri sergilemektedir. Aynı zamanda, yatırımcı davranışları, sürü psikolojisi ve sosyal medya etkisi gibi değişkenler de fiyat tahminlerini zorlaştırmaktadır. Bu çalışmada, SARIMA modelinin artık değerleri, doğrusal olmayan desenlerin modellenmesi için XGBoost’a girdi olarak verilmiştir. Böylece, hem doğrusal hem de doğrusal olmayan desenler aynı modelde bütünleştirilmiştir. Makale, BTC (Bitcoin), XRP (Ripple) ve ETH (Ethereum) fiyat verileri üzerinde modelin performansını analiz etmektedir. Model performansı, Akaike Bilgi Kriteri (AIC), Ortalama Kare Hata (MSE) ve Ortalama Mutlak Hata (MAE) ölçütleriyle değerlendirilmiştir. Sonuçlar, SARIMA-XGBoost modelinin bağımsız SARIMA ve XGBoost modellerine kıyasla daha yüksek doğruluk sağladığını göstermektedir. Özellikle volatil dönemlerde hibrit model, kripto para piyasasının dinamiklerini daha iyi yakalayarak daha küçük hata oranları üretmektedir. Bu çalışmanın bulguları, kripto para birimlerinin gelecekteki fiyatlarının tahmin edilmesi için hibrit modellemelerin etkili bir yaklaşım olduğunu kanıtlamaktadır. Aynı zamanda, optimize edilmiş hibrit modellerin yatırım kararları ve risk yönetimi süreçlerinde kullanılabilirliği vurgulanmaktadır. Ancak, kısa vadeli dalgalanmalar ve aşırı volatilite dönemlerinde modelin performansında iyileştirmeler yapılabileceği belirtilmektedir.

Keywords

• SARIMA
• XGBoost
• Kripto Para Tahmini
• Finansal Zaman Serisi
• Volatilite
• Hibrit Modelleme

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