Bitcoin Price Prediction Using Cuckoo Search Algorithm for Feature Selection with LSTM Model

Abstract

Deep learning is now a top method for predicting the price of Bitcoin, providing an excellent and well thought out strategy. This technique makes use of a wealth of historical data from reliable sources covering the first ten years of Bitcoin. Technical data and complex patterns are deeply integrated into the dataset to enhance the model's comprehension of complex Bitcoin price patterns. These additions greatly deepen our understanding of the various factors affecting the price of Bitcoin. This method is based on a strategic procedure that involves the careful selection of certain high-performing characteristics. These traits go through a stringent screening process that uses advanced algorithms such as the Cuckoo algorithm. The chosen features are then smoothly incorporated into an encoder-decoder model of LSTM (Long Short-Term Memory), improving the model's ability to predict Bitcoin prices. In this case, it is critical to apply deep learning methods, especially the LSTM encoder-decoder model. Through the integration of technical indicators and sophisticated selection techniques with historical Bitcoin data, this approach leverages deep learning capabilities to identify intricate patterns in Bitcoin pricing movements. In the end, this gives the model the ability to forecast future changes in the price of Bitcoin with accuracy.