Published Paper Details:

Cryptocurrency price prediction using Long Short-Term Memory (LSTM) is a popular method for forecasting future prices of the cryptocurrency It is supervised and organised by a blockchain, a peer-to-peer network that also acts as a secure record of transactions.. LSTM is a type of recurrent neural network (RNN) that is capable of learning long-term dependencies, making it well-suited for time series data like financial markets. In this approach, historical price data is used to train the LSTM model, which can then be used to make predictions about future price movements.To implement this, the first step is to preprocess the data, which involves scaling and transforming the data into a format that can be fed into the LSTM model. Then, using a sliding window technique, the LSTM model is supervised on the preprocessed data to predict the following value in the time series on the basis of a certain number of earlier values. After training, the model can be used to make predictions about future price movements. These predictions can be evaluated using various performance metrics such as mean squared error or root mean squared error to assess the accuracy of the model. Overall, the use of LSTM for Bitcoin price prediction has shown promising results, with some studies reporting high levels of accuracy in predicting future price movements. However, it is important to note that cryptocurrency markets are highly volatile and unpredictable, and there are many factors beyond historical price data that can affect the price of Bitcoin