Measurement and forecasting of fluctuating Cryptocurrency prices using deep learning

In the context of cryptocurrency forecasting, this paper provides a comprehensive analysis of various prediction methods, including financial methods, statistical methods, machine learning, and deep learning. It investigates the causes of the effectiveness of the most well-known and accurate techniques. In addition, the study compares the results of its RMSE, MAE, and MAPE to those of other studies that have used the same dataset with same time period. This study investigates the effect of different evaluation matrices on the accuracy of models and compares the performance of two distinct cryptocurrencies on various deep-learning models. By analyzing the relationship between evaluation metrics and the accuracy of price predictions, the study aims to facilitate the development of more precise models for predicting the prices of cryptocurrencies. This study adds to the literature on cryptocurrency forecasting by evaluating several approaches to see which methods provide the most reliable results. Researchers and practitioners can make informed decisions regarding the development and application of cryptocurrencies if they comprehend the factors that contribute to the accuracy of cryptocurrency prediction models. In addition, the study emphasizes how bi-LSTM and LSTM can be used to forecast various cryptocurrencies and how price fluctuations can be measured and predicted with an accuracy level greater than 80%. Overall, this study contributes to the advancement of knowledge and the development of cryptocurrency price prediction methods, thereby augmenting decision-making processes in cryptocurrency markets.