A comparative audit of 15+ time series architectures (ARIMA-GARCH, XGBoost, Transformers) across four temporal domains. Focuses on predictive robustness, residual adequacy (Ljung-Box, ARCH-LM), and statistical significance testing.
data-science machine-learning deep-learning cross-validation transformers pytorch xgboost forecasting statistical-modeling time-series-forecasting mlops arima-garch residual-diagnostics transformer-garch
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Updated
May 1, 2026 - Jupyter Notebook