logo

Blog


You can expect to read a post here every Sunday or Monday, mostly about Techtonique. Want to get notified? Subscribe via RSS.

A list of previous posts can be found below, and a list of categories here. Use the search bar appearing before this paragraph if you're looking for a more specific keyword.

You can also me on GitHub or Hire me on LinkedIn or Hire me on Malt or Hire me on Fiverr or Hire me on Upwork

Having any other inquiry related to the content published here? Easiest/fastest/safest way is to send an email to: thierry dot moudiki at gmail dot com.



Blogroll | Top

List of posts by date of publication | Top

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