Published Paper Details:

The behaviors of cryptocurrency markets are dynamic and complicated, with high volatility and a wide range of influencing factors. In order to anticipate bitcoin prices, this study investigates the use of deep learning techniques, particularly recurrent neural networks (RNNs) and long short- term memory networks (LSTMs). For well-known cryptocurrencies, the study makes use of past price data, trade volumes, and extra technical indicators. The methodology includes feature engineering, training the model, evaluation, and preprocessing of the data. Measures like Mean Absolute Error (MAE) and Mean Squared Error (MSE) are used to evaluate the performance of the deep learning model. The results shed light on how well deep learning captures patterns and temporal relationships in cryptocurrency price data. The conversation explores how the findings could affect real-world trading tactics, points out its shortcomings, and suggests directions for further study. This study adds to the expanding corpus of research on the prediction of cryptocurrency prices by providing a sophisticated knowledge of the potential benefits and difficulties of using deep learning in this field.