Mevsimsel Trendler ve Piyasa Volatilitesinin Entegrasyonu: Kripto Para Tahmini için Hibrit SARIMA-XGBoost Modeli
Integrating Seasonal Trends and Market Volatility: A Hybrid SARIMA-XGBoost Model for Cryptocurrency Forecasting

Summary/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 decisionmaking 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.

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