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Blogroll | Top

List of posts by date of publication | Top

  1. Native uncertainty quantification for time series with NGBoost Oct 8, 2025
  2. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving Oct 6, 2025
  3. Real-time pricing with a pretrained probabilistic stock return model Oct 1, 2025
  4. Combining any model with GARCH(1,1) for probabilistic stock forecasting Sep 23, 2025
  5. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R Sep 21, 2025
  6. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning Sep 16, 2025
  7. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation Sep 9, 2025
  8. Transfer Learning using ahead::ridge2f on synthetic stocks returns Sep 8, 2025
  9. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London... Sep 4, 2025
  10. external regressors in ahead::dynrmf's interface for Machine learning forecasting Sep 1, 2025
  11. 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
  12. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting Jul 26, 2025
  13. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization) Jul 15, 2025
  14. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI. Jul 10, 2025
  15. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics). Jul 8, 2025
  16. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting Jul 7, 2025
  17. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting Jul 6, 2025
  18. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting Jul 5, 2025
  19. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting Jul 4, 2025
  20. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting Jul 3, 2025
  21. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting Jul 2, 2025
  22. 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
  23. R version of 'Backpropagating quasi-randomized neural networks' Jun 24, 2025
  24. Backpropagating quasi-randomized neural networks Jun 23, 2025
  25. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting Jun 21, 2025
  26. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf Jun 18, 2025
  27. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters Jun 14, 2025
  28. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models Jun 13, 2025
  29. Exceptionally, and on a more personal note (otherwise I may get buried alive)... Jun 10, 2025
  30. Free R, Python and SQL editors in techtonique dot net Jun 9, 2025
  31. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation Jun 7, 2025
  32. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 6, 2025
  33. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 5, 2025
  34. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 4, 2025
  35. 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
  36. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH) Jun 2, 2025
  37. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line) Jun 1, 2025
  38. 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
  39. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios May 30, 2025
  40. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose May 29, 2025
  41. 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
  42. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API May 27, 2025
  43. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API May 26, 2025
  44. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor May 20, 2025
  45. Survival stacking: survival analysis translated as supervised classification in R and Python May 5, 2025
  46. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package Apr 25, 2025
  47. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction Apr 21, 2025
  48. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf Apr 20, 2025
  49. 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
  50. Extending the Theta forecasting method to GLMs and attention Apr 8, 2025
  51. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models) Mar 31, 2025
  52. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net Mar 28, 2025
  53. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize Mar 25, 2025
  54. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance Mar 19, 2025
  55. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels Mar 16, 2025
  56. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction Mar 9, 2025
  57. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion Mar 8, 2025
  58. CRAN-like repository for most recent releases of Techtonique's R packages Mar 2, 2025
  59. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier Feb 27, 2025
  60. Web app with DeepSeek R1 and Hugging Face API for chatting Feb 23, 2025
  61. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R) Feb 17, 2025
  62. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction) Feb 12, 2025
  63. 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
  64. A simple test of the martingale hypothesis in esgtoolkit Feb 3, 2025
  65. Command Line Interface (CLI) for techtonique.net's API Jan 31, 2025
  66. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend Jan 27, 2025
  67. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition Jan 20, 2025
  68. Techtonique is out! (with a tutorial in various programming languages and formats) Jan 14, 2025
  69. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN Jan 14, 2025
  70. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Jan 5, 2025
  71. 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
  72. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 29, 2024
  73. No-code Machine Learning Cross-validation and Interpretability in techtonique.net Dec 23, 2024
  74. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce Dec 15, 2024
  75. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt Dec 9, 2024
  76. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting Dec 1, 2024
  77. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Nov 24, 2024
  78. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates') Nov 23, 2024
  79. GLMNet in Python: Generalized Linear Models Nov 18, 2024
  80. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
  81. Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
  82. Chat with your tabular data in www.techtonique.net Oct 30, 2024
  83. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
  84. R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
  85. R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
  86. Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
  87. Gradient-Boosting anything (alert: high performance) Oct 6, 2024
  88. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
  89. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
  90. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
  91. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
  92. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
  93. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
  94. New versions of nnetsauce and ahead Sep 9, 2024
  95. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
  96. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
  97. R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
  98. A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
  99. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
  100. Bayesian (nonlinear) adaptive learning Aug 12, 2024
  101. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
  102. Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
  103. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
  104. learningmachine for Python (new version) Jul 15, 2024
  105. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
  106. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
  107. 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
  108. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
  109. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
  110. Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
  111. Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
  112. Forecasting the Economy May 27, 2024
  113. A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
  114. Probability of receiving a loan; using learningmachine May 12, 2024
  115. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
  116. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
  117. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
  118. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
  119. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
  120. learningmachine v1.1.2: for Python Apr 1, 2024
  121. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
  122. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
  123. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
  124. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
  125. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
  126. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
  127. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
  128. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
  129. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
  130. Subsampling continuous and discrete response variables Jan 22, 2024
  131. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
  132. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
  133. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
  134. 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
  135. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
  136. Diffusion models in Python with esgtoolkit Dec 11, 2023
  137. Julia packaging at the command line Dec 4, 2023
  138. Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
  139. A plethora of datasets at your fingertips Nov 20, 2023
  140. A classifier that's very accurate (and deep) Nov 12, 2023
  141. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
  142. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
  143. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
  144. Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
  145. A diffusion model: G2++ Oct 9, 2023
  146. Diffusion models in ESGtoolkit + announcements Oct 2, 2023
  147. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
  148. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
  149. Forecasting in Python with ahead Sep 11, 2023
  150. Risk-neutralize simulations Sep 4, 2023
  151. Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
  152. Reminder Apr 30, 2023
  153. Did you ask ChatGPT about who you are? Apr 16, 2023
  154. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
  155. Simple interfaces to the forecasting API Nov 23, 2022
  156. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
  157. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
  158. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
  159. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
  160. A Machine Learning workflow using Techtonique Jun 6, 2022
  161. Super Mario Bros © in the browser using PyScript May 8, 2022
  162. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
  163. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
  164. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
  165. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
  166. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
  167. Forecasting with `ahead` (Python version) Dec 13, 2021
  168. Tuning and interpreting LSBoost Nov 15, 2021
  169. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
  170. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
  171. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
  172. Forecasting with `ahead` Oct 15, 2021
  173. Classification using linear regression Sep 26, 2021
  174. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
  175. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
  176. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
  177. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
  178. Hyperparameters tuning with GPopt Jun 11, 2021
  179. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
  180. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
  181. Bayesian Optimization with GPopt Apr 16, 2021
  182. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
  183. Explaining xgboost predictions with the teller Mar 12, 2021
  184. An infinity of time series models in nnetsauce Mar 6, 2021
  185. New activation functions in mlsauce's LSBoost Feb 12, 2021
  186. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
  187. A deeper learning architecture in nnetsauce Dec 18, 2020
  188. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
  189. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
  190. Generalized nonlinear models in nnetsauce Nov 28, 2020
  191. Boosting nonlinear penalized least squares Nov 21, 2020
  192. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
  193. NEWS Oct 30, 2020
  194. A glimpse into my PhD journey Oct 23, 2020
  195. Submitting R package to CRAN Oct 16, 2020
  196. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
  197. Forecasting lung disease progression Oct 2, 2020
  198. New nnetsauce Sep 25, 2020
  199. Technical documentation Sep 18, 2020
  200. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
  201. Back next week, and a few announcements Sep 4, 2020
  202. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
  203. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
  204. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
  205. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
  206. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
  207. Announcements Jun 26, 2020
  208. Parallel AdaOpt classification Jun 19, 2020
  209. Comments section and other news Jun 12, 2020
  210. Maximizing your tip as a waiter Jun 5, 2020
  211. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
  212. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
  213. AdaOpt May 15, 2020
  214. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
  215. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
  216. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
  217. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
  218. Grid search cross-validation using crossval Apr 10, 2020
  219. Documentation for the querier, a query language for Data Frames Apr 3, 2020
  220. Time series cross-validation using crossval Mar 27, 2020
  221. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
  222. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
  223. R notebooks for nnetsauce Mar 6, 2020
  224. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
  225. Create a specific feed in your Jekyll blog Feb 21, 2020
  226. Git/Github for contributing to package development Feb 14, 2020
  227. Feedback forms for contributing Feb 7, 2020
  228. nnetsauce for R Jan 31, 2020
  229. A new version of nnetsauce (v0.3.1) Jan 24, 2020
  230. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
  231. Search bar, new year 2020 Jan 10, 2020
  232. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
  233. Understanding model interactions with the `teller` Dec 13, 2019
  234. Using the `teller` on a classifier Dec 6, 2019
  235. Benchmarking the querier's verbs Nov 29, 2019
  236. Composing the querier's verbs for data wrangling Nov 22, 2019
  237. Comparing and explaining model predictions with the teller Nov 15, 2019
  238. Tests for the significance of marginal effects in the teller Nov 8, 2019
  239. Introducing the teller Nov 1, 2019
  240. Introducing the querier Oct 25, 2019
  241. Prediction intervals for nnetsauce models Oct 18, 2019
  242. Using R in Python for statistical learning/data science Oct 11, 2019
  243. Model calibration with `crossval` Oct 4, 2019
  244. Bagging in the nnetsauce Sep 25, 2019
  245. Adaboost learning with nnetsauce Sep 18, 2019
  246. Change in blog's presentation Sep 4, 2019
  247. nnetsauce on Pypi Jun 5, 2019
  248. More nnetsauce (examples of use) May 9, 2019
  249. nnetsauce Mar 13, 2019
  250. crossval Mar 13, 2019
  251. test Mar 10, 2019

Categories | Top

Forecasting

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

Misc

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

Python

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