Okcan/time_series_forecasting_model

🧠 Bitcoin Price Forecasting using LSTM Neural Network

A deep learning model based on Long Short-Term Memory (LSTM) networks to predict the next-day closing price of Bitcoin (BTC-USD) using historical data from Yahoo Finance.

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🔍 Model Overview

Feature	Description
📦 Model Type	LSTM (Long Short-Term Memory), a variant of Recurrent Neural Networks (RNN)
🧠 Frameworks Used	TensorFlow (Keras API), Scikit-learn, NumPy, Pandas, yfinance
📈 Input	Past 60 days of Bitcoin closing prices
🎯 Output	Predicted closing price for the next day
📊 Evaluation	Root Mean Squared Error (RMSE)
🧪 Goal	Short-term (1-day ahead) BTC price forecasting

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🔧 What the Model Does

• Downloads historical BTC-USD data from Yahoo Finance
• Normalizes the data between 0 and 1 using MinMaxScaler
• Splits into 80% training and 20% test sets
• Creates time-sequenced inputs with a 60-day sliding window
• Trains a 2-layer LSTM model with dropout to prevent overfitting
• Evaluates the model using RMSE
• Plots predicted vs actual prices
• Makes a next-day prediction using the last 60 days of data

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💡 Use Cases

• Educational: Learning time series forecasting and LSTM models
• Research: Benchmarking for financial forecasting models
• Visualization: Analyze model performance on real BTC data
• Academic Support: Useful for papers or prototypes on AI-based financial systems

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⚠️ Limitations

• Uses only the closing price (no volume, indicators, or sentiment data)
• Performs only single-step (1-day ahead) forecasting
• Does not account for sudden market news or shocks
• Not designed for high-frequency or live trading systems

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🚀 Potential Improvements

• Include additional features: volume, RSI, MACD, etc.
• Integrate external signals: news, social media sentiment, macro data
• Add attention or transformer-based layers
• Extend to multi-step forecasting (3-day, 5-day, etc.)
• Deploy as REST API or interactive dashboard
• Connect to Binance or other exchanges for live predictions

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📁 Files

• lstm_bitcoin_predictor.py: Full code to train, evaluate, and predict using LSTM
• data.csv: (optional) Cached historical BTC-USD data
• model.h5: Saved trained model

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📜 License

This project is licensed under the MIT License.

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This model is intended for educational and research purposes only.

It is not designed for financial or investment decision-making.
No guarantees are made about the accuracy of the forecasts.
The authors accept no responsibility for any financial losses incurred from the use of this model.
Use at your own risk.