TimeGPT’s Potential in Cryptocurrency Forecasting: Efficiency, Accuracy, and Economic Value

Minxing Wang (), Pavel Braslavski and Dmitry I. Ignatov
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Minxing Wang: Laboratory for Models and Methods of Computational Pragmatics, School of Data Analysis and AI, Faculty of Computer Science, HSE University, 11 Pokrovskiy Boulevard, Moscow 109028, Russia
Pavel Braslavski: Laboratory for Models and Methods of Computational Pragmatics, School of Data Analysis and AI, Faculty of Computer Science, HSE University, 11 Pokrovskiy Boulevard, Moscow 109028, Russia
Dmitry I. Ignatov: Laboratory for Models and Methods of Computational Pragmatics, School of Data Analysis and AI, Faculty of Computer Science, HSE University, 11 Pokrovskiy Boulevard, Moscow 109028, Russia

Forecasting, 2025, vol. 7, issue 3, 1-19

Abstract: Accurate and efficient cryptocurrency price prediction is vital for investors in the volatile crypto market. This study comprehensively evaluates nine models—including baseline, zero-shot, and deep learning architectures—on 21 major cryptocurrencies using daily and hourly data. Our multi-dimensional evaluation assesses models based on prediction accuracy (MAE, RMSE, MAPE), speed, statistical significance (Diebold–Mariano test), and economic value (Sharpe Ratio). Our research found that the optimally fine-tuned TimeGPT model (without variables) demonstrated superior performance across both Daily and Hourly datasets, with its statistical leadership confirmed by the Diebold–Mariano test. Fine-tuned Chronos excelled in daily predictions, while TFT was a close second to TimeGPT for hourly forecasts. Crucially, zero-shot models like TimeGPT and Chronos were tens of times faster than traditional deep learning models, offering high accuracy with superior computational efficiency. A key finding from our economic analysis is that a model’s effectiveness is highly dependent on market characteristics. For instance, TimeGPT with variables showed exceptional profitability in the volatile ETH market, whereas the zero-shot Chronos model was the top performer for the cyclical BTC market. This also highlights that variables have asset-specific effects with TimeGPT: improving predictions for ICP, LTC, OP, and DOT, but hindering UNI, ATOM, BCH, and ARB. Recognizing that prior research has overemphasized prediction accuracy, this study provides a more holistic and practical standard for model evaluation by integrating speed, statistical significance, and economic value. Our findings collectively underscore TimeGPT’s immense potential as a leading solution for cryptocurrency forecasting, offering a top-tier balance of accuracy and efficiency. This multi-dimensional approach provides critical, theoretical, and practical guidance for investment decisions and risk management, proving especially valuable in real-time trading scenarios.

Keywords: TimeGPT; cryptocurrency prediction; machine learning; time series; zero-shot method; deep learning (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
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