Designing a deep learning model for bitcoin price trend prediction in dynamic markets

The market for cryptocurrencies, definitely Bitcoin, is dishonourable for its life-threatening unpredictability. A deep learning-based background for forecasting cryptocurrency responsibility arrangements over innumerable time epochs and market circumstances is accessible in this work. The prototype seeks to increase accuracy in approximating both short & long-term alternations in prices by manipulating cutting-edge DL algorithms and providing insights into various market scenarios like bear & bull markets. In this paper, we present a deep learning-based model for calculating Bitcoin price trends that is intended for self-motivated market environments. To improve prediction stability, the model uses a time-based deep learning structure and multiple data features. Experimental consequences show the projected method achieves 94.2% recognition accuracy, reduces the error rate by 5.8% and outperforms baseline long short-term memory (LSTM) and convolutional neural network (CNN) models across numerous evaluation metrics. These consequences establish the construction in highly unpredictable cryptocurrency markets.