The 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.
python machine-learning random-forest numpy scikit-learn arma pandas seaborn matplotlib feature-engineering arima statsmodels time-series-analysis randomforestregressor augmented-dickey-fuller-test mplfina
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Updated
Feb 18, 2025 - Jupyter Notebook