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Blogroll | Top

List of posts by date of publication | Top

  1. Model-agnostic prediction intervals in Python and R: does nnetsauce's QuantileRegressor hold up? Sep 14, 2026
  2. ahead (Time Series Forecasting with uncertainty quantification) gets a lot faster to install: most dependencies are now optional Sep 8, 2026
  3. Skip the R/Python runtime: fast tabular dashboards with Observable Framework Aug 31, 2026
  4. PCARVFL vs CTGAN for synthetic tabular data generation on an insurance pricing dataset Aug 22, 2026
  5. 'Zero-Shot Probabilistic Stock Returns Forecasting with Pretrained RVFL Networks' accepted at COPA 2026 (and to appear in the Proceedings of Machine Learning Research) Aug 15, 2026
  6. 'PCARVFLSimulator': a GAN-like tabular data synthesizer built from PCA scores, a Random Vector Functional-Link network, and residuals bootstrapping Aug 10, 2026
  7. 'garchf': GARCH probabilistic forecasting with package 'forecast'-style interface (and 'rugarch' under the hood) Aug 1, 2026
  8. GPopt for R: Bayesian and conformal optimization of black-box functions and hyperparameter tuning Jul 26, 2026
  9. My last R posts: How conformalization helps weak models, fast conformal prediction with jackknife+ (and no refitting), and sklearn in R Jul 13, 2026
  10. Natively Interpretable Boosting Jul 12, 2026
  11. Fast conformal prediction (no refitting) for some Machine Learning models via closed-form jackknife plus Jun 27, 2026
  12. Using scikit-learn models in R easily with the tisthemachinelearner package Jun 21, 2026
  13. No-Code Machine Learning in Excel with the Techtonique API Jun 14, 2026
  14. How Conformal Prediction Makes Linear Models Good Enough — An Example Using R Package mlS3 Jun 7, 2026
  15. Techtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now) May 31, 2026
  16. Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R May 21, 2026
  17. Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R May 17, 2026
  18. Probabilistic Time Series Cross-Validation with R package crossvalidation May 16, 2026
  19. One interface, (Almost) Every Classifier (and Regressor): unifiedml v0.3.0 May 9, 2026
  20. You Don't Need to Learn All the Weights on tabular data: The Case for rvflnet (a nonlinear expressive glmnet) on regression, classification and survival analysis May 2, 2026
  21. Survival analysis with sklearn, glmnet, keras, pytorch, lightgbm, xgboost, nnetsauce, mlsauce Part 2 Apr 28, 2026
  22. Any Sklearn Regressor as a Survival Model — Does It Actually Work? Benchmarking vs Established Packages Apr 26, 2026
  23. Conformal Optimization Beats Bayesian Optimization, Optuna and Random Search on 72 classification Datasets Apr 19, 2026
  24. `mlS3` — A Unified S3 Machine Learning Interface in R Apr 12, 2026
  25. One interface, (Almost) Every Classifier: unifiedml v0.2.1 Apr 4, 2026
  26. Techtonique dot net is down until further notice Apr 1, 2026
  27. Explaining Time-Series Forecasts with Sensitivity Analysis (ahead::dynrmf and external regressors) Mar 29, 2026
  28. Python version of 'Option pricing using time series models as market price of risk Pt.3' Mar 22, 2026
  29. Option pricing using time series models as market price of risk Pt.3 Mar 16, 2026
  30. Explaining Time-Series Forecasts with Exact Shapley Values (ahead::dynrmf with external regressors applied to scenarios) Mar 8, 2026
  31. My Presentation at Risk 2026: Lightweight Transfer Learning for Financial Forecasting Mar 1, 2026
  32. nnetsauce with and without jax for GPU acceleration Feb 23, 2026
  33. Understanding Boosted Configuration Networks (combined neural networks and boosting): An Intuitive Guide Through Their Hyperparameters Feb 16, 2026
  34. R version of Python package survivalist, for model-agnostic survival analysis Feb 9, 2026
  35. Presenting Lightweight Transfer Learning for Financial Forecasting (Risk 2026) Feb 4, 2026
  36. Option pricing using time series models as market price of risk Feb 1, 2026
  37. Enhancing Time Series Forecasting (ahead::ridge2f) with Attention-Based Context Vectors (ahead::contextridge2f) Jan 31, 2026
  38. Overfitting and scaling (on GPU T4) tests on nnetsauce.CustomRegressor Jan 29, 2026
  39. Beyond Cross-validation: Hyperparameter Optimization via Generalization Gap Modeling Jan 25, 2026
  40. GPopt for Machine Learning (hyperparameters' tuning) Jan 21, 2026
  41. rtopy: an R to Python bridge -- novelties Jan 8, 2026
  42. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation' Jan 3, 2026
  43. Forecasting benchmark: Dynrmf (a new serious competitor in town) vs Theta Method on M-Competitions and Tourism competitition Jan 1, 2026
  44. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce Dec 17, 2025
  45. Overfitting Random Fourier Features: Universal Approximation Property Dec 13, 2025
  46. Counterfactual Scenario Analysis with ahead::ridge2f Dec 11, 2025
  47. Zero-Shot Probabilistic Time Series Forecasting with TabPFN 2.5 and nnetsauce Dec 10, 2025
  48. ARIMA Pricing: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint) Dec 7, 2025
  49. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis) Dec 3, 2025
  50. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn Dec 1, 2025
  51. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv Nov 21, 2025
  52. unifiedml: A Unified Machine Learning Interface for R, is now on CRAN + Discussion about AI replacing humans Nov 16, 2025
  53. Context-aware Theta forecasting Method: Extending Classical Time Series Forecasting with Machine Learning Nov 13, 2025
  54. unifiedml in R: A Unified Machine Learning Interface Nov 5, 2025
  55. Deterministic Shift Adjustment in Arbitrage-Free Pricing (historical to risk-neutral short rates) Oct 28, 2025
  56. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation Oct 27, 2025
  57. RPweave: Unified R + Python + LaTeX System using uv Oct 19, 2025
  58. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator Oct 19, 2025
  59. R port of llama2.c Oct 9, 2025
  60. Native uncertainty quantification for time series with NGBoost Oct 8, 2025
  61. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving Oct 6, 2025
  62. Real-time pricing with a pretrained probabilistic stock return model Oct 1, 2025
  63. Combining any model with GARCH(1,1) for probabilistic stock forecasting Sep 23, 2025
  64. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R Sep 21, 2025
  65. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning Sep 16, 2025
  66. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation Sep 9, 2025
  67. Transfer Learning using ahead::ridge2f on synthetic stocks returns Sep 8, 2025
  68. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London... Sep 4, 2025
  69. external regressors in ahead::dynrmf's interface for Machine learning forecasting Sep 1, 2025
  70. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation' Aug 20, 2025
  71. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting Jul 26, 2025
  72. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization) Jul 15, 2025
  73. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI. Jul 10, 2025
  74. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics). Jul 8, 2025
  75. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting Jul 7, 2025
  76. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting Jul 6, 2025
  77. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting Jul 5, 2025
  78. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting Jul 4, 2025
  79. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting Jul 3, 2025
  80. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting Jul 2, 2025
  81. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian) Jul 1, 2025
  82. R version of 'Backpropagating quasi-randomized neural networks' Jun 24, 2025
  83. Backpropagating quasi-randomized neural networks Jun 23, 2025
  84. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting Jun 21, 2025
  85. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf Jun 18, 2025
  86. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters Jun 14, 2025
  87. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models Jun 13, 2025
  88. Free R, Python and SQL editors in techtonique dot net Jun 9, 2025
  89. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation Jun 7, 2025
  90. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 6, 2025
  91. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 5, 2025
  92. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 4, 2025
  93. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH) Jun 3, 2025
  94. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH) Jun 2, 2025
  95. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line) Jun 1, 2025
  96. 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 May 31, 2025
  97. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios May 30, 2025
  98. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose May 29, 2025
  99. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out May 28, 2025
  100. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API May 27, 2025
  101. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API May 26, 2025
  102. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor May 20, 2025
  103. Survival stacking: survival analysis translated as supervised classification in R and Python May 5, 2025
  104. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package Apr 25, 2025
  105. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction Apr 21, 2025
  106. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf Apr 20, 2025
  107. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series) Apr 14, 2025
  108. Extending the Theta forecasting method to GLMs and attention Apr 8, 2025
  109. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models) Mar 31, 2025
  110. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net Mar 28, 2025
  111. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize Mar 25, 2025
  112. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance Mar 19, 2025
  113. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels Mar 16, 2025
  114. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction Mar 9, 2025
  115. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion Mar 8, 2025
  116. CRAN-like repository for most recent releases of Techtonique's R packages Mar 2, 2025
  117. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier Feb 27, 2025
  118. Web app with DeepSeek R1 and Hugging Face API for chatting Feb 23, 2025
  119. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R) Feb 17, 2025
  120. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction) Feb 12, 2025
  121. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart Feb 10, 2025
  122. A simple test of the martingale hypothesis in esgtoolkit Feb 3, 2025
  123. Command Line Interface (CLI) for techtonique.net's API Jan 31, 2025
  124. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend Jan 27, 2025
  125. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition Jan 20, 2025
  126. Techtonique is released! (with a tutorial in various programming languages and formats) Jan 14, 2025
  127. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN Jan 14, 2025
  128. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Jan 5, 2025
  129. Python and Interactive dashboard version of Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 31, 2024
  130. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 29, 2024
  131. No-code Machine Learning Cross-validation and Interpretability in techtonique.net Dec 23, 2024
  132. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce Dec 15, 2024
  133. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt Dec 9, 2024
  134. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting Dec 1, 2024
  135. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Nov 24, 2024
  136. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates') Nov 23, 2024
  137. GLMNet in Python: Generalized Linear Models Nov 18, 2024
  138. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
  139. Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
  140. Chat with your tabular data in www.techtonique.net Oct 30, 2024
  141. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
  142. R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
  143. R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
  144. Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
  145. Gradient-Boosting anything (alert: high performance) Oct 6, 2024
  146. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
  147. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
  148. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
  149. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
  150. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
  151. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
  152. New versions of nnetsauce and ahead Sep 9, 2024
  153. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
  154. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
  155. R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
  156. A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
  157. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
  158. Bayesian (nonlinear) adaptive learning Aug 12, 2024
  159. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
  160. Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
  161. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
  162. learningmachine for Python (new version) Jul 15, 2024
  163. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
  164. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
  165. 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
  166. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
  167. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
  168. Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
  169. Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
  170. Forecasting the Economy May 27, 2024
  171. A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
  172. Probability of receiving a loan; using learningmachine May 12, 2024
  173. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
  174. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
  175. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
  176. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
  177. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
  178. learningmachine v1.1.2: for Python Apr 1, 2024
  179. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
  180. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
  181. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
  182. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
  183. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
  184. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
  185. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
  186. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
  187. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
  188. Subsampling continuous and discrete response variables Jan 22, 2024
  189. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
  190. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
  191. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
  192. A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction Dec 25, 2023
  193. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
  194. Diffusion models in Python with esgtoolkit Dec 11, 2023
  195. Julia packaging at the command line Dec 4, 2023
  196. Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
  197. A plethora of datasets at your fingertips Nov 20, 2023
  198. A classifier that's very accurate (and deep) Nov 12, 2023
  199. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
  200. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
  201. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
  202. Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
  203. A diffusion model: G2++ Oct 9, 2023
  204. Diffusion models in ESGtoolkit + announcements Oct 2, 2023
  205. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
  206. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
  207. Forecasting in Python with ahead Sep 11, 2023
  208. Risk-neutralize simulations Sep 4, 2023
  209. Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
  210. Reminder Apr 30, 2023
  211. Did you ask ChatGPT about who you are? Apr 16, 2023
  212. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
  213. Simple interfaces to the forecasting API Nov 23, 2022
  214. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
  215. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
  216. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
  217. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
  218. A Machine Learning workflow using Techtonique Jun 6, 2022
  219. Super Mario Bros © in the browser using PyScript May 8, 2022
  220. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
  221. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
  222. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
  223. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
  224. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
  225. Forecasting with `ahead` (Python version) Dec 13, 2021
  226. Tuning and interpreting LSBoost Nov 15, 2021
  227. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
  228. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
  229. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
  230. Forecasting with `ahead` Oct 15, 2021
  231. Classification using linear regression Sep 26, 2021
  232. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
  233. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
  234. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
  235. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
  236. Hyperparameters tuning with GPopt Jun 11, 2021
  237. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
  238. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
  239. Bayesian Optimization with GPopt Apr 16, 2021
  240. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
  241. Explaining xgboost predictions with the teller Mar 12, 2021
  242. An infinity of time series models in nnetsauce Mar 6, 2021
  243. New activation functions in mlsauce's LSBoost Feb 12, 2021
  244. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
  245. A deeper learning architecture in nnetsauce Dec 18, 2020
  246. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
  247. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
  248. Generalized nonlinear models in nnetsauce Nov 28, 2020
  249. Boosting nonlinear penalized least squares Nov 21, 2020
  250. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
  251. NEWS Oct 30, 2020
  252. A glimpse into my PhD journey Oct 23, 2020
  253. Submitting R package to CRAN Oct 16, 2020
  254. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
  255. Forecasting lung disease progression Oct 2, 2020
  256. New nnetsauce Sep 25, 2020
  257. Technical documentation Sep 18, 2020
  258. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
  259. Back next week, and a few announcements Sep 4, 2020
  260. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
  261. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
  262. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
  263. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
  264. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
  265. Announcements Jun 26, 2020
  266. Parallel AdaOpt classification Jun 19, 2020
  267. Comments section and other news Jun 12, 2020
  268. Maximizing your tip as a waiter Jun 5, 2020
  269. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
  270. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
  271. AdaOpt May 15, 2020
  272. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
  273. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
  274. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
  275. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
  276. Grid search cross-validation using crossval Apr 10, 2020
  277. Documentation for the querier, a query language for Data Frames Apr 3, 2020
  278. Time series cross-validation using crossval Mar 27, 2020
  279. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
  280. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
  281. R notebooks for nnetsauce Mar 6, 2020
  282. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
  283. Create a specific feed in your Jekyll blog Feb 21, 2020
  284. Git/Github for contributing to package development Feb 14, 2020
  285. Feedback forms for contributing Feb 7, 2020
  286. nnetsauce for R Jan 31, 2020
  287. A new version of nnetsauce (v0.3.1) Jan 24, 2020
  288. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
  289. Search bar, new year 2020 Jan 10, 2020
  290. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
  291. Understanding model interactions with the `teller` Dec 13, 2019
  292. Using the `teller` on a classifier Dec 6, 2019
  293. Benchmarking the querier's verbs Nov 29, 2019
  294. Composing the querier's verbs for data wrangling Nov 22, 2019
  295. Comparing and explaining model predictions with the teller Nov 15, 2019
  296. Tests for the significance of marginal effects in the teller Nov 8, 2019
  297. Introducing the teller Nov 1, 2019
  298. Introducing the querier Oct 25, 2019
  299. Prediction intervals for nnetsauce models Oct 18, 2019
  300. Using R in Python for statistical learning/data science Oct 11, 2019
  301. Model calibration with `crossval` Oct 4, 2019
  302. Bagging in the nnetsauce Sep 25, 2019
  303. Adaboost learning with nnetsauce Sep 18, 2019
  304. Change in blog's presentation Sep 4, 2019
  305. nnetsauce on Pypi Jun 5, 2019
  306. More nnetsauce (examples of use) May 9, 2019
  307. nnetsauce Mar 13, 2019
  308. crossval Mar 13, 2019
  309. test Mar 10, 2019

Categories | Top

Forecasting

  1. ARIMA Pricing: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint)
  2. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  3. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  4. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition
  5. Techtonique is released! (with a tutorial in various programming languages and formats)
  6. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on?
  7. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  8. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  9. 10 uncertainty quantification methods in nnetsauce forecasting
  10. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  11. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  12. DeepMTS, a Deep Learning Model for Multivariate Time Series
  13. Version v0.14.0 of nnetsauce for R and Python
  14. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  15. (News from) forecasting in Python with ahead (progress bars and plots)
  16. Forecasting in Python with ahead
  17. Simple interfaces to the forecasting API
  18. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  19. A dashboard illustrating bivariate time series forecasting with `ahead`
  20. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret

Misc

  1. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  2. A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction
  3. Julia packaging at the command line
  4. A plethora of datasets at your fingertips
  5. Risk-neutralize simulations
  6. Comparing cross-validation results using crossval_ml and boxplots
  7. Reminder
  8. Did you ask ChatGPT about who you are?
  9. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  10. Boosted Configuration (_neural_) Networks for classification
  11. Super Mario Bros © in the browser using PyScript
  12. News from ESGtoolkit, ycinterextra, and nnetsauce
  13. Time series cross-validation using `crossvalidation` (Part 2)
  14. Fast and scalable forecasting with ahead::ridge2f
  15. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  16. Forecasting with `ahead`
  17. Documentation and source code for GPopt, a package for Bayesian optimization
  18. Hyperparameters tuning with GPopt
  19. A forecasting tool (API) with examples in curl, R, Python
  20. Bayesian Optimization with GPopt Part 2 (save and resume)
  21. Bayesian Optimization with GPopt
  22. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  23. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  24. NEWS
  25. A glimpse into my PhD journey
  26. Forecasting lung disease progression
  27. New nnetsauce
  28. Technical documentation
  29. A new version of nnetsauce, and a new Techtonique website
  30. Back next week, and a few announcements
  31. Maximizing your tip as a waiter (Part 2)
  32. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees
  33. Announcements
  34. Comments section and other news
  35. Maximizing your tip as a waiter
  36. Custom errors for cross-validation using crossval::crossval_ml
  37. Encoding your categorical variables based on the response variable and correlations
  38. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  39. Grid search cross-validation using crossval
  40. Time series cross-validation using crossval
  41. On model specification, identification, degrees of freedom and regularization
  42. Create a specific feed in your Jekyll blog
  43. Git/Github for contributing to package development
  44. Feedback forms for contributing
  45. Change in blog's presentation
  46. test

Python

  1. Model-agnostic prediction intervals in Python and R: does nnetsauce's QuantileRegressor hold up?
  2. ahead (Time Series Forecasting with uncertainty quantification) gets a lot faster to install: most dependencies are now optional
  3. Skip the R/Python runtime: fast tabular dashboards with Observable Framework
  4. PCARVFL vs CTGAN for synthetic tabular data generation on an insurance pricing dataset
  5. 'PCARVFLSimulator': a GAN-like tabular data synthesizer built from PCA scores, a Random Vector Functional-Link network, and residuals bootstrapping
  6. Natively Interpretable Boosting
  7. Fast conformal prediction (no refitting) for some Machine Learning models via closed-form jackknife plus
  8. Techtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now)
  9. Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R
  10. Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R
  11. Survival analysis with sklearn, glmnet, keras, pytorch, lightgbm, xgboost, nnetsauce, mlsauce Part 2
  12. Any Sklearn Regressor as a Survival Model — Does It Actually Work? Benchmarking vs Established Packages
  13. Conformal Optimization Beats Bayesian Optimization, Optuna and Random Search on 72 classification Datasets
  14. Techtonique dot net is down until further notice
  15. Python version of 'Option pricing using time series models as market price of risk Pt.3'
  16. nnetsauce with and without jax for GPU acceleration
  17. Understanding Boosted Configuration Networks (combined neural networks and boosting): An Intuitive Guide Through Their Hyperparameters
  18. R version of Python package survivalist, for model-agnostic survival analysis
  19. Overfitting and scaling (on GPU T4) tests on nnetsauce.CustomRegressor
  20. GPopt for Machine Learning (hyperparameters' tuning)
  21. rtopy: an R to Python bridge -- novelties
  22. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  23. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce
  24. Overfitting Random Fourier Features: Universal Approximation Property
  25. Zero-Shot Probabilistic Time Series Forecasting with TabPFN 2.5 and nnetsauce
  26. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  27. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn
  28. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv
  29. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation
  30. RPweave: Unified R + Python + LaTeX System using uv
  31. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  32. Native uncertainty quantification for time series with NGBoost
  33. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving
  34. Real-time pricing with a pretrained probabilistic stock return model
  35. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  36. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  37. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  38. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  39. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  40. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting
  41. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization)
  42. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI.
  43. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  44. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  45. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  46. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  47. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  48. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  49. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  50. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  51. Backpropagating quasi-randomized neural networks
  52. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  53. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
  54. Free R, Python and SQL editors in techtonique dot net
  55. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  56. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  57. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  58. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  59. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  60. 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
  61. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  62. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  63. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  64. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  65. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  66. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  67. Survival stacking: survival analysis translated as supervised classification in R and Python
  68. Extending the Theta forecasting method to GLMs and attention
  69. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net
  70. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  71. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  72. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  73. Web app with DeepSeek R1 and Hugging Face API for chatting
  74. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  75. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  76. Command Line Interface (CLI) for techtonique.net's API
  77. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  78. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition
  79. Techtonique is released! (with a tutorial in various programming languages and formats)
  80. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  81. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on?
  82. Python and Interactive dashboard version of Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  83. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  84. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  85. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  86. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  87. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  88. GLMNet in Python: Generalized Linear Models
  89. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  90. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  91. Chat with your tabular data in www.techtonique.net
  92. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  93. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  94. R and Python consoles + JupyterLite in www.techtonique.net
  95. Gradient-Boosting anything (alert: high performance)
  96. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  97. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  98. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  99. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  100. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  101. New versions of nnetsauce and ahead
  102. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  103. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  104. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  105. Copulas for uncertainty quantification in time series forecasting
  106. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  107. learningmachine for Python (new version)
  108. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  109. 10 uncertainty quantification methods in nnetsauce forecasting
  110. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  111. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  112. Automated hyperparameter tuning using any conformalized surrogate
  113. Recognizing handwritten digits with Ridge2Classifier
  114. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  115. Probability of receiving a loan; using learningmachine
  116. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  117. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  118. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  119. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  120. learningmachine v1.1.2: for Python
  121. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  122. rtopy (v0.1.1): calling R functions in Python
  123. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  124. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  125. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  126. Tuning Machine Learning models with GPopt's new version Part 2
  127. Tuning Machine Learning models with GPopt's new version
  128. Subsampling continuous and discrete response variables
  129. DeepMTS, a Deep Learning Model for Multivariate Time Series
  130. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  131. A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction
  132. Diffusion models in Python with esgtoolkit (Part2)
  133. Diffusion models in Python with esgtoolkit
  134. Quasi-randomized nnetworks in Julia, Python and R
  135. A plethora of datasets at your fingertips
  136. A classifier that's very accurate (and deep)
  137. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  138. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  139. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  140. Version v0.14.0 of nnetsauce for R and Python
  141. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  142. (News from) forecasting in Python with ahead (progress bars and plots)
  143. Forecasting in Python with ahead
  144. Did you ask ChatGPT about who you are?
  145. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  146. Simple interfaces to the forecasting API
  147. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  148. Prediction intervals (not only) for Boosted Configuration Networks in Python
  149. A Machine Learning workflow using Techtonique
  150. Super Mario Bros © in the browser using PyScript
  151. News from ESGtoolkit, ycinterextra, and nnetsauce
  152. Explaining a Keras _neural_ network predictions with the-teller
  153. New version of nnetsauce -- various quasi-randomized networks
  154. A dashboard illustrating bivariate time series forecasting with `ahead`
  155. Forecasting with `ahead` (Python version)
  156. Tuning and interpreting LSBoost
  157. Classification using linear regression
  158. Documentation and source code for GPopt, a package for Bayesian optimization
  159. Hyperparameters tuning with GPopt
  160. A forecasting tool (API) with examples in curl, R, Python
  161. Bayesian Optimization with GPopt Part 2 (save and resume)
  162. Bayesian Optimization with GPopt
  163. Compatibility of nnetsauce and mlsauce with scikit-learn
  164. Explaining xgboost predictions with the teller
  165. An infinity of time series models in nnetsauce
  166. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  167. A deeper learning architecture in nnetsauce
  168. Generalized nonlinear models in nnetsauce
  169. Boosting nonlinear penalized least squares
  170. Technical documentation
  171. A new version of nnetsauce, and a new Techtonique website
  172. Back next week, and a few announcements
  173. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  174. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  175. nnetsauce version 0.5.0, randomized neural networks on GPU
  176. Maximizing your tip as a waiter (Part 2)
  177. Parallel AdaOpt classification
  178. Maximizing your tip as a waiter
  179. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  180. AdaOpt
  181. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  182. Encoding your categorical variables based on the response variable and correlations
  183. Documentation for the querier, a query language for Data Frames
  184. Import data into the querier (now on Pypi), a query language for Data Frames
  185. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  186. A new version of nnetsauce (v0.3.1)
  187. Using R in Python for statistical learning/data science
  188. nnetsauce on Pypi

QuasiRandomizedNN

  1. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  2. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  3. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  4. 10 uncertainty quantification methods in nnetsauce forecasting
  5. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  6. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  7. Automated hyperparameter tuning using any conformalized surrogate
  8. Recognizing handwritten digits with Ridge2Classifier
  9. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  10. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  11. DeepMTS, a Deep Learning Model for Multivariate Time Series
  12. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  13. Quasi-randomized nnetworks in Julia, Python and R
  14. A classifier that's very accurate (and deep)
  15. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  16. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  17. Version v0.14.0 of nnetsauce for R and Python
  18. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  19. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  20. New version of nnetsauce -- various quasi-randomized networks
  21. Tuning and interpreting LSBoost
  22. Classification using linear regression
  23. An infinity of time series models in nnetsauce
  24. A deeper learning architecture in nnetsauce
  25. Classify penguins with nnetsauce's MultitaskClassifier
  26. Bayesian forecasting for uni/multivariate time series
  27. New nnetsauce
  28. Technical documentation
  29. A new version of nnetsauce, and a new Techtonique website
  30. Back next week, and a few announcements
  31. nnetsauce version 0.5.0, randomized neural networks on GPU
  32. R notebooks for nnetsauce
  33. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  34. Feedback forms for contributing
  35. nnetsauce for R
  36. A new version of nnetsauce (v0.3.1)
  37. 2019 Recap, the nnetsauce, the teller and the querier
  38. Prediction intervals for nnetsauce models
  39. Bagging in the nnetsauce
  40. Adaboost learning with nnetsauce
  41. nnetsauce on Pypi
  42. More nnetsauce (examples of use)
  43. nnetsauce

R

  1. Model-agnostic prediction intervals in Python and R: does nnetsauce's QuantileRegressor hold up?
  2. ahead (Time Series Forecasting with uncertainty quantification) gets a lot faster to install: most dependencies are now optional
  3. Skip the R/Python runtime: fast tabular dashboards with Observable Framework
  4. 'Zero-Shot Probabilistic Stock Returns Forecasting with Pretrained RVFL Networks' accepted at COPA 2026 (and to appear in the Proceedings of Machine Learning Research)
  5. 'garchf': GARCH probabilistic forecasting with package 'forecast'-style interface (and 'rugarch' under the hood)
  6. GPopt for R: Bayesian and conformal optimization of black-box functions and hyperparameter tuning
  7. My last R posts: How conformalization helps weak models, fast conformal prediction with jackknife+ (and no refitting), and sklearn in R
  8. Fast conformal prediction (no refitting) for some Machine Learning models via closed-form jackknife plus
  9. Using scikit-learn models in R easily with the tisthemachinelearner package
  10. How Conformal Prediction Makes Linear Models Good Enough — An Example Using R Package mlS3
  11. Techtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now)
  12. Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R
  13. Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R
  14. Probabilistic Time Series Cross-Validation with R package crossvalidation
  15. One interface, (Almost) Every Classifier (and Regressor): unifiedml v0.3.0
  16. You Don't Need to Learn All the Weights on tabular data: The Case for rvflnet (a nonlinear expressive glmnet) on regression, classification and survival analysis
  17. `mlS3` — A Unified S3 Machine Learning Interface in R
  18. One interface, (Almost) Every Classifier: unifiedml v0.2.1
  19. Techtonique dot net is down until further notice
  20. Explaining Time-Series Forecasts with Sensitivity Analysis (ahead::dynrmf and external regressors)
  21. Option pricing using time series models as market price of risk Pt.3
  22. Explaining Time-Series Forecasts with Exact Shapley Values (ahead::dynrmf with external regressors applied to scenarios)
  23. My Presentation at Risk 2026: Lightweight Transfer Learning for Financial Forecasting
  24. nnetsauce with and without jax for GPU acceleration
  25. Understanding Boosted Configuration Networks (combined neural networks and boosting): An Intuitive Guide Through Their Hyperparameters
  26. R version of Python package survivalist, for model-agnostic survival analysis
  27. Presenting Lightweight Transfer Learning for Financial Forecasting (Risk 2026)
  28. Option pricing using time series models as market price of risk
  29. Enhancing Time Series Forecasting (ahead::ridge2f) with Attention-Based Context Vectors (ahead::contextridge2f)
  30. Beyond Cross-validation: Hyperparameter Optimization via Generalization Gap Modeling
  31. rtopy: an R to Python bridge -- novelties
  32. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  33. Forecasting benchmark: Dynrmf (a new serious competitor in town) vs Theta Method on M-Competitions and Tourism competitition
  34. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce
  35. Overfitting Random Fourier Features: Universal Approximation Property
  36. Counterfactual Scenario Analysis with ahead::ridge2f
  37. ARIMA Pricing: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint)
  38. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  39. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn
  40. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv
  41. unifiedml: A Unified Machine Learning Interface for R, is now on CRAN + Discussion about AI replacing humans
  42. Context-aware Theta forecasting Method: Extending Classical Time Series Forecasting with Machine Learning
  43. unifiedml in R: A Unified Machine Learning Interface
  44. Deterministic Shift Adjustment in Arbitrage-Free Pricing (historical to risk-neutral short rates)
  45. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation
  46. RPweave: Unified R + Python + LaTeX System using uv
  47. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  48. R port of llama2.c
  49. Native uncertainty quantification for time series with NGBoost
  50. Real-time pricing with a pretrained probabilistic stock return model
  51. Combining any model with GARCH(1,1) for probabilistic stock forecasting
  52. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R
  53. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  54. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  55. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  56. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  57. external regressors in ahead::dynrmf's interface for Machine learning forecasting
  58. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  59. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  60. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  61. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  62. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  63. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  64. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  65. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  66. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  67. R version of 'Backpropagating quasi-randomized neural networks'
  68. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  69. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf
  70. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models
  71. Free R, Python and SQL editors in techtonique dot net
  72. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  73. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  74. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  75. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  76. 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
  77. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  78. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  79. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  80. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  81. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  82. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  83. Survival stacking: survival analysis translated as supervised classification in R and Python
  84. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  85. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  86. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  87. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  88. Extending the Theta forecasting method to GLMs and attention
  89. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  90. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  91. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  92. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  93. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  94. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  95. CRAN-like repository for most recent releases of Techtonique's R packages
  96. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  97. Web app with DeepSeek R1 and Hugging Face API for chatting
  98. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  99. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  100. A simple test of the martingale hypothesis in esgtoolkit
  101. Command Line Interface (CLI) for techtonique.net's API
  102. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  103. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition
  104. Techtonique is released! (with a tutorial in various programming languages and formats)
  105. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on?
  106. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  107. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  108. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  109. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  110. GLMNet in Python: Generalized Linear Models
  111. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  112. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  113. Chat with your tabular data in www.techtonique.net
  114. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  115. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  116. R and Python consoles + JupyterLite in www.techtonique.net
  117. Gradient-Boosting anything (alert: high performance): Part2, R version
  118. Gradient-Boosting anything (alert: high performance)
  119. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  120. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  121. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  122. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  123. New versions of nnetsauce and ahead
  124. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  125. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  126. R package development workflow (assuming you're using macOS or Linux)
  127. A new method for deriving a nonparametric confidence interval for the mean
  128. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  129. Bayesian (nonlinear) adaptive learning
  130. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  131. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  132. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  133. Forecasting the Economy
  134. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  135. Probability of receiving a loan; using learningmachine
  136. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  137. Conformalized predictive simulations for univariate time series on more than 250 data sets
  138. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  139. Multiple examples of Machine Learning forecasting with ahead
  140. rtopy (v0.1.1): calling R functions in Python
  141. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  142. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  143. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  144. A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction
  145. Diffusion models in Python with esgtoolkit (Part2)
  146. Diffusion models in Python with esgtoolkit
  147. Quasi-randomized nnetworks in Julia, Python and R
  148. A plethora of datasets at your fingertips
  149. A classifier that's very accurate (and deep)
  150. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  151. Version v0.14.0 of nnetsauce for R and Python
  152. A diffusion model: G2++
  153. Diffusion models in ESGtoolkit + announcements
  154. Risk-neutralize simulations
  155. Comparing cross-validation results using crossval_ml and boxplots
  156. Did you ask ChatGPT about who you are?
  157. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  158. Simple interfaces to the forecasting API
  159. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  160. Boosted Configuration (neural) Networks Pt. 2
  161. Boosted Configuration (_neural_) Networks for classification
  162. News from ESGtoolkit, ycinterextra, and nnetsauce
  163. New version of nnetsauce -- various quasi-randomized networks
  164. A dashboard illustrating bivariate time series forecasting with `ahead`
  165. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  166. Time series cross-validation using `crossvalidation` (Part 2)
  167. Fast and scalable forecasting with ahead::ridge2f
  168. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  169. Forecasting with `ahead`
  170. `crossvalidation` and random search for calibrating support vector machines
  171. parallel grid search cross-validation using `crossvalidation`
  172. `crossvalidation` on R-universe, plus a classification example
  173. A forecasting tool (API) with examples in curl, R, Python
  174. An infinity of time series models in nnetsauce
  175. New activation functions in mlsauce's LSBoost
  176. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  177. Classify penguins with nnetsauce's MultitaskClassifier
  178. Bayesian forecasting for uni/multivariate time series
  179. Boosting nonlinear penalized least squares
  180. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  181. Submitting R package to CRAN
  182. Simulation of dependent variables in ESGtoolkit
  183. Forecasting lung disease progression
  184. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  185. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  186. nnetsauce version 0.5.0, randomized neural networks on GPU
  187. Maximizing your tip as a waiter (Part 2)
  188. Maximizing your tip as a waiter
  189. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  190. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  191. Custom errors for cross-validation using crossval::crossval_ml
  192. Encoding your categorical variables based on the response variable and correlations
  193. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  194. Grid search cross-validation using crossval
  195. Time series cross-validation using crossval
  196. On model specification, identification, degrees of freedom and regularization
  197. R notebooks for nnetsauce
  198. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  199. Feedback forms for contributing
  200. nnetsauce for R
  201. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  202. Using R in Python for statistical learning/data science
  203. Model calibration with `crossval`
  204. crossval