This is the source codes and modeling data for paper: LSTM + Transformer Real-Time Crash Risk Evaluation Using Traffic Flow and Risky Driving Behavior Data
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Updated
Feb 18, 2025 - Jupyter Notebook
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crash-prediction
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End-to-End Python implementation of LPPLS (Log-Periodic Power Law Singularity) framework for detecting financial bubbles and critical transitions. Features Filimonov-Sornette calibration, Lagrange regularization, Lomb-Scargle spectral validation, and Monte Carlo significance testing. Complete computational replication of Hosseinzadeh (2025).
python numpy pandas scientific-computing scipy monte-carlo-simulation quantitative-finance complex-systems time-series-analysis financial-mathematics bubble-detection econophysics systemic-risk emerging-markets crash-prediction lppls financial-bubbles grid-search-optimization isolated-markets lomb-scargle
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Dec 20, 2025 - Jupyter Notebook
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