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This project explores historical Bitcoin price data from September 2014 to July 2021, implementing various technical indicators and forecasting models.\n\n## Key Features\n- Technical indicator implementation (Bollinger Bands, MACD, Stochastic Oscillator)\n- Time series analysis with ARIMA modeling\n- Feature importance analysis using Random Forest\n- Stationarity testing and seasonal decomposition\n- Price prediction with confidence intervals\n\n## Technical Components\n- Time series decomposition and stationarity analysis\n- ARMA model parameter optimization\n- Volatility impact analysis on model performance\n- Interactive visualizations using mplfinance\n\n## Tools \u0026 Technologies\n- Python\n- Pandas \u0026 NumPy\n- Scikit-learn\n- Statsmodels\n- Matplotlib \u0026 Seaborn\n- mplfinance\n\n## Results\nThe analysis reveals the challenges of predicting Bitcoin prices during highly volatile periods and demonstrates how traditional time series models perform under different market conditions. The project includes comparative analysis of model performance during stable and volatile market phases.\n\n## Usage\nThe Jupyter notebook contains detailed analysis and code implementation, including:\n- Data preprocessing\n- Technical indicator calculation\n- Model training and evaluation\n- Visualization of results\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Furvee1810%2Fbitcoin-price-forecasting-using-arma","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Furvee1810%2Fbitcoin-price-forecasting-using-arma","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Furvee1810%2Fbitcoin-price-forecasting-using-arma/lists"}