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Blogroll | Top

List of posts by date of publication | Top

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

Categories | Top

Forecasting

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