Cryptocurrency Price Prediction using Bi-LSTM

This project aims to predict the future prices of cryptocurrencies using a Bidirectional Long Short-Term Memory (Bi-LSTM) model. The program will take a CSV file as input, which must contain two columns: Date and Close. It will then predict the closing price for the next year, visualize the prediction with a graph, and evaluate the model's performance using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE).

Table of Contents

• Installation
• Usage
• Input File Format
• Output
• Evaluation Metrics
• Contributing

Installation

1. Clone the repository:

   git clone https://github.com/your-username/cryptocurrency-price-prediction.git
   cd cryptocurrency-price-prediction

2. Install the required dependencies:

   pip install -r requirements.txt

3. Ensure you have TensorFlow and Keras installed for running the Bi-LSTM model:

   pip install tensorflow keras

Usage

1. Run the program and provide a CSV file as input:

   python predict.py --input_file path/to/your/inputfile.csv

2. The program will:

   • Preprocess the data.
   • Train a Bi-LSTM model.
   • Predict the next one year of cryptocurrency prices.
   • Plot a graph showing actual vs. predicted values.
   • Print performance metrics such as MSE, RMSE, and MAPE.

Input File Format

The input CSV file should contain at least two columns:

1. Date: The date of the cryptocurrency price (in YYYY-MM-DD format).
2. Close: The closing price of the cryptocurrency for that date.

Example:

Date	Close
2023-01-01	17000.5
2023-01-02	17250.3
2023-01-03	16890.8

Make sure the dates are in sequential order without missing any values, as this is required for the time-series prediction.

Output

1. Graph of Predictions:
   A graph will be generated showing the actual closing prices and predicted prices for the next year.

   Sample Output:

   

2. Performance Metrics:
   The program will output the following metrics:

   • MSE (Mean Squared Error): The average squared difference between the actual and predicted values.
   • RMSE (Root Mean Squared Error): The square root of the average squared differences between the actual and predicted values.
   • MAPE (Mean Absolute Percentage Error): The average of the absolute percentage errors between the actual and predicted values.

   Example:

   MSE: 23456.78
   RMSE: 153.21
   MAPE: 2.45%

Evaluation Metrics

• MSE (Mean Squared Error): Measures the average squared difference between actual and predicted values.

  Formula: MSE = (1/n) * Σ(actual - predicted)^2

• RMSE (Root Mean Squared Error): The square root of MSE, providing error in the same units as the original data.

  Formula: RMSE = √MSE

• MAPE (Mean Absolute Percentage Error): Measures the accuracy of predictions as a percentage.

  Formula: MAPE = (1/n) * Σ(|(actual - predicted) / actual|) * 100

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests. When contributing, please ensure your code follows the project's coding style and includes relevant tests.