A Review and Taxonomy on Forecasting Crypto Prices Based on Machine Learning and Deep Learning Models

Approved

Relevance and Originality

The paper addresses a highly relevant and timely topic in the realm of finance and technology: crypto price prediction. Given the volatility of cryptocurrency markets and their growing importance in global finance, the exploration of prediction methodologies is both necessary and original. The focus on the challenges of traditional statistical methods versus modern machine learning techniques provides fresh insights into the ongoing evolution of predictive analytics in finance.

Methodology

The review primarily synthesizes existing literature on machine learning techniques for crypto price prediction, which is appropriate for a review paper. However, the paper could benefit from a clearer explanation of the selection criteria for the literature included in the review. Details about the databases searched, the time frame of the studies considered, and the methodologies of those studies would enhance the transparency and credibility of the review. Additionally, it would be useful to categorize the approaches discussed based on specific attributes, such as algorithms used, datasets, and performance metrics.

Validity & Reliability

To ensure the validity and reliability of the findings, the paper should critically assess the quality of the studies reviewed. This includes evaluating the datasets used, the robustness of the machine learning models applied, and the methodologies employed in the studies. Discussing the limitations of the existing research and the potential biases in the data sources would provide a more nuanced understanding of the challenges in crypto price prediction.

Clarity and Structure

The paper is generally clear in its presentation, but improving the structure could enhance readability. A well-defined organization with distinct sections—such as Introduction, Literature Review, Methodology, Findings, and Conclusion—would help guide the reader through the content. The use of headings and subheadings can aid in breaking down complex information and making key points more accessible. Including visual elements, such as graphs or tables summarizing findings from the reviewed literature, would further clarify the presented information.

Result Analysis

The analysis of contemporary data-driven approaches is a strong aspect of the paper. However, it would be beneficial to delve deeper into the implications of the identified research gaps. Discussing how these gaps can be addressed in future research and what impact that could have on the field would provide valuable insights. The conclusion should not only summarize the findings but also suggest practical applications of the reviewed methodologies, potential avenues for future research, and their relevance to investors and financial analysts. This would enhance the contribution of the paper to both academic and practical discussions surrounding cryptocurrency forecasting.