NextIn035846/BitCoin_cryptocurrency

Time Series Data Prediction using Long Short-Term Memory (LSTM)

LSTM Time Series

Introduction

This repository demonstrates the use of Long Short-Term Memory (LSTM), a type of Recurrent Neural Network (RNN), for time series data prediction. Specifically, it focuses on predicting the value of Bitcoin, a popular cryptocurrency, based on historical price data. LSTM is particularly effective for handling time series data due to its ability to capture long-term dependencies and patterns.

Model Architecture

The LSTM model used for Bitcoin price prediction consists of multiple LSTM layers followed by one or more fully connected layers. The model is designed to learn the temporal patterns and dependencies present in the time series data.

The architecture can be customized by modifying the BitCoin_using LSTM.ipynb file according to your specific use case.