Comparison of Modern Methods for Cryptocurrency Return Forecasting with SARIMA Benchmark
Abstract
This paper presents a comparative analysis of modern methods for forecasting cryptocurrency returns. It considers classical statistical models (SARIMA), machine learning techniques, neural network architectures (LSTM, Transformers), and the Group Method of Data Handling (GMDH). The aim is to identify the strengths and limitations of different approaches in short- and medium-term forecasting.
The empirical analysis is based on time series of 13 major cryptocurrencies over 3 to 10 years. Data were transformed into logarithmic returns, and block cross-validation with automated hyperparameter tuning was applied. Forecasting horizons of 1, 7, and 30 days were examined, along with recursive and scalable multi-step forecasting methods.
Results show that GMDH models achieved the highest accuracy on short-term horizons, while LSTM consistently underperformed. For monthly forecasts, the Chronos Transformer, applied in a few-shot in-context learning regime, outperformed other models. SARIMA remained a reliable benchmark on medium horizons. Scalable approaches reduced error accumulation compared to recursive forecasting.
These findings highlight the absence of a universal algorithm and emphasize the importance of selecting methods according to horizon and analytical objectives.
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