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Blogroll | Top

List of posts by date of publication | Top

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