Predict the future trend of cryptocurrencies using an LSTM-based deep learning model trained on historical price data. This project demonstrates how recurrent neural networks can be used to forecast time series data, especially in volatile markets like crypto.
- Uses LSTM (Long Short-Term Memory) networks for time-series forecasting
- Preprocesses historical price data for input into neural network
- Trains and tests the model on cleaned crypto datasets
- Plots predicted vs actual prices for evaluation
- Modular and easy to extend for different coins or timeframes
LSTM (Long Short-Term Memory) is a type of RNN (Recurrent Neural Network) designed to capture long-term dependencies. This makes it well-suited for financial time-series prediction, where understanding previous trends is crucial.