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Blogroll | Top

List of posts by date of publication | Top

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