In recent years, cryptocurrencies have received substantial attention from investors, researchers and the media due to their volatile behaviour and potential for high returns. This interest has led to an expanding body of research aimed at predicting cryptocurrency prices, which are notably influenced by a wide array of technical, sentimental, and legal factors. This paper reviews scholarly content from 2014 to 2024, employing a systematic approach to explore advanced quantitative methods for cryptocurrency price prediction. It encompasses a broad spectrum of predictive models, from early statistical analyses to sophisticated machine and deep learning algorithms. Notably, this review identifies and discusses the integration of emerging technologies such as Transformers and hybrid deep learning models, which offer new avenues for enhancing prediction accuracy and practical applicability in real-world scenarios. By thoroughly investigating various methodologies and parameters influencing cryptocurrency price predictions, including market sentiment, technical indicators, and blockchain features, this review highlights the field’s complexity and rapid evolution. The analysis identifies significant research gaps and under-explored areas, providing a foundational guideline for future studies. These guidelines aim to connect theoretical advancements with practical, profit-driven applications in cryptocurrency trading, ensuring that future research is both innovative and applicable.

In recent years, cryptocurrencies have received substantial attention from investors, researchers and the media due to their volatile behaviour and potential for high returns. This interest has led to an expanding body of research aimed at predicting cryptocurrency prices, which are notably influenced by a wide array of technical, sentimental, and legal factors. This paper reviews scholarly content from 2014 to 2024, employing a systematic approach to explore advanced quantitative methods for cryptocurrency price prediction. It encompasses a broad spectrum of predictive models, from early statistical analyses to sophisticated machine and deep learning algorithms. Notably, this review identifies and discusses the integration of emerging technologies such as Transformers and hybrid deep learning models, which offer new avenues for enhancing prediction accuracy and practical applicability in real-world scenarios. By thoroughly investigating various methodologies and parameters influencing cryptocurrency price predictions, including market sentiment, technical indicators, and blockchain features, this review highlights the field’s complexity and rapid evolution. The analysis identifies significant research gaps and under-explored areas, providing a foundational guideline for future studies. These guidelines aim to connect theoretical advancements with practical, profit-driven applications in cryptocurrency trading, ensuring that future research is both innovative and applicable.

Accurately forecasting future trends in complex, high-dimensional time series is challenging, as the predictive accuracy of models hinges on selecting appropriate historical data windows. While existing research in fields such as finance and energy forecasting has advanced from simple linear models to sophisticated machine learning and deep learning architectures, most current approaches rely on static, arbitrarily chosen window sizes. This limitation prevents models from adapting to rapidly evolving conditions and may dilute the impact of recent, highly informative data. To overcome these shortcomings, this work introduces a dynamic window sizing framework that adjusts window lengths in real time, guided by changes in volatility. Using cryptocurrency markets as an illustrative case study -- where high volatility and complex dynamics demand agile methodologies -- the proposed approach identifies optimal window sizes for distinct volatility levels and integrates them into a hybrid deep learning Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network (LSTM-GRU) model. This enables the model to align its training perspective with evolving market states, capturing both transient and persistent patterns more effectively than static-window counterparts. Experimental evaluations show demonstrate that dynamic window sizing consistently outperforms all tested static window approaches by yielding lower mean squared error (MSE) and mean absolute error (MAE), along with higher directional accuracy. Specifically, the dynamic model attained an MSE of 0.1749, an MAE of 0.2281, and a directional accuracy of 56.77%, outperforming the best static model, which recorded an MSE of 0.1933, an MAE of 0.2883, and a directional accuracy of 49.11%. By offering a robust mechanism for aligning forecasting models with current data characteristics, the proposed adaptive strategy addresses the limitations of static window sizing and sets a new benchmark for handling rapidly changing data. Moreover, this approach establishes a generalisable framework that can enhance decision-making and risk mitigation in diverse domains where adaptability and precision are critical.These results highlight the effectiveness of adopting an adaptive approach in scenarios characterised by frequent changes in data patterns, thereby underscoring the importance of dynamic methodologies over fixed-window configurations.