Forecasting lung disease progression
https://thierrymoudiki.github.io/blog/2020/10/02/misc/r/osic-kaggle
@teeeeeeeee.bsky.social
@teeeeeeeee.bsky.socialForecasting lung disease progression
https://thierrymoudiki.github.io/blog/2020/10/02/misc/r/osic-kaggle
@teeeeeeeee.bsky.socialStatistical/Machine Learning explainability using Kernel Ridge Regression surrogates
https://thierrymoudiki.github.io/blog/2020/11/06/explainableml/r/misc/xai-krr-surrogate
@teeeeeeeee.bsky.socialTechtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now)
https://thierrymoudiki.github.io/blog/2026/05/31/r/python/techtonique/techtonique-dot-net-is-back
@teeeeeeeee.bsky.socialDeepMTS, a Deep Learning Model for Multivariate Time Series
https://thierrymoudiki.github.io/blog/2024/01/15/python/quasirandomizednn/forecasting/DeepMTS
@teeeeeeeee.bsky.socialARIMA Pricing: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint)
https://thierrymoudiki.github.io/blog/2025/12/07/r/forecasting/ARIMA-Pricing
@teeeeeeeee.bsky.socialPython version of 'Option pricing using time series models as market price of risk Pt.3'
https://thierrymoudiki.github.io/blog/2026/03/22/python/python-Semi-parametric-MarketPriceofRisk-update
@teeeeeeeee.bsky.social'PCARVFLSimulator': a GAN-like tabular data synthesizer built from PCA scores, a Random Vector Functional-Link network, and residuals bootstrapping
https://thierrymoudiki.github.io/blog/2026/08/10/python/PCARVLsimulator
@teeeeeeeee.bsky.socialProbabilistic Time Series Cross-Validation with R package crossvalidation
https://thierrymoudiki.github.io/blog/2026/05/16/r/crossvalidation
@teeeeeeeee.bsky.socialexternal regressors in ahead::dynrmf's interface for Machine learning forecasting
https://thierrymoudiki.github.io/blog/2025/09/01/r/external-regressors-in-dynrmf
@teeeeeeeee.bsky.socialExtending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
https://thierrymoudiki.github.io/blog/2025/04/14/r/theta-M1-M3-M4
@teeeeeeeee.bsky.socialMy last R posts: How conformalization helps weak models, fast conformal prediction with jackknife+ (and no refitting), and sklearn in R
https://thierrymoudiki.github.io/blog/2026/07/13/r/my-last-R-posts
@teeeeeeeee.bsky.socialAdaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
https://thierrymoudiki.github.io/blog/2024/09/10/python/r/adaptive-polyak
@teeeeeeeee.bsky.socialTuning Machine Learning models with GPopt's new version Part 2
https://thierrymoudiki.github.io/blog/2024/02/05/python/gpopt-new2
@teeeeeeeee.bsky.socialNew instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation
https://thierrymoudiki.github.io/blog/2025/10/27/r/python/deterministic-shift-caps-swaptions
@teeeeeeeee.bsky.socialConformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R
https://thierrymoudiki.github.io/blog/2026/05/21/r/python/Conformalized-TabICL-nnetsauce
@teeeeeeeee.bsky.socialAdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
https://thierrymoudiki.github.io/blog/2020/05/22/r/adaopt/mlsauce/adaopt-classifier-2
@teeeeeeeee.bsky.socialComparing and explaining model predictions with the teller
https://thierrymoudiki.github.io/blog/2019/11/15/explainableml/the-teller-3
@teeeeeeeee.bsky.socialBayesian forecasting for uni/multivariate time series
https://thierrymoudiki.github.io/blog/2020/12/04/r/quasirandomizednn/bayesian-forecasting
@teeeeeeeee.bsky.social@teeeeeeeee.bsky.socialNative uncertainty quantification for time series with NGBoost
https://thierrymoudiki.github.io/blog/2025/10/08/r/python/ngboost-time-series
