Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
A flexible hybrid approach to probabilistic stock forecasting that combines statistical model with ARCH effects, offering an alternative to traditional ARMA-GARCH models
Python-bloggers
Data science news and tutorials - contributed by Python bloggers
Articles by T. Moudiki
An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
Comparing XGBoost's default hyperparameters with GenericBooster + LinearRegression hyperparameters to analyze overfitting patterns in balanced accuracy metrics
Exceptionally, and on a more personal note (otherwise I may get buried alive)…
Exceptionally, and on a more personal note (otherwise I may get buried alive)... [...Read more...]
Free R, Python and SQL editors in techtonique dot net
A 'video' showcasing the use of free R, Python and SQL editors in techtonique dot net [...Read more...]
Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
Examples of use of Probabilistic Machine Learning (for longitudinal data) Reserving with scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce
Machine Learning (for longitudinal data) Reserving (work in progress)
Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
A flexible hybrid approach to probabilistic stock forecasting that combines machine learning with ARCH effects, offering an alternative to traditional ARMA-GARCH models
Permutations and SHAPley values for feature importance in techtonique dot net’s API (with R + Python + the command line)
How to use techtonique.net's API to compute feature importance using permutations and SHAPley values
Which patient is going to survive longer? Another guide to using techtonique dot net’s API (with R + Python + the command line) for survival analysis
How to make API calls to techtonique.net for survival analysis and plot the results with rush
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