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Koosha, E., Seighaly, M. and Abbasi, E. (2022) Measuring the Accuracy and Precision of Random Forest, Long Short-Term Memory, and Recurrent Neural Network Models in Predicting the Top and Bottom of Bitcoin Price. Journal of Mathematics and Modeling in Finance, 2, 107-128.
https://doi.org/10.22054/jmmf.2023.15183

has been cited by the following article:

• TITLE: Predicting Future Cryptocurrency Prices Using Machine Learning Algorithms

  AUTHORS: Vaibhav Saha

  KEYWORDS: Cryptocurrency Price Prediction, Machine Learning Algorithms, Feature Engineering, Performance Metrics

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.11 No.4, November 10, 2023

  ABSTRACT: Cryptocurrency price prediction has garnered significant attention due to the growing importance of digital assets in the financial landscape. This paper presents a comprehensive study on predicting future cryptocurrency prices using machine learning algorithms. Open-source historical data from various cryptocurrency exchanges is utilized. Interpolation techniques are employed to handle missing data, ensuring the completeness and reliability of the dataset. Four technical indicators are selected as features for prediction. The study explores the application of five machine learning algorithms to capture the complex patterns in the highly volatile cryptocurrency market. The findings demonstrate the strengths and limitations of the different approaches, highlighting the significance of feature engineering and algorithm selection in achieving accurate cryptocurrency price predictions. The research contributes valuable insights into the dynamic and rapidly evolving field of cryptocurrency price prediction, assisting investors and traders in making informed decisions amidst the challenges posed by the cryptocurrency market.