Forecasting in Python with ahead

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A new Python version of ahead is now available on GitHub and PyPI. ahead is a package (Python and R) for univariate and multivariate time series forecasting, with uncertainty quantification (in particular, simulation-based).

Here are the new features:

• Align with R version (see https://github.com/Techtonique/ahead/blob/main/NEWS.md#version-070 and https://github.com/Techtonique/ahead/blob/main/NEWS.md#version-080) as much as possible

• moving block bootstrap in ridge2f, basicf, in addition to circular block bootstrap from 0.6.2 for obtaining predictive simulations

• adjust R-Vine copulas on residuals for ridge2f simulation (with empirical and Gaussian marginals)

It still takes forever to be imported in Python (only the first time) when the R packages are not available in the environment. But I’ll release a workaround soon.

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Previous publications

1. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce Dec 15, 2024
2. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt Dec 9, 2024
3. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting Dec 1, 2024
4. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Nov 24, 2024
5. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates') Nov 23, 2024
6. GLMNet in Python: Generalized Linear Models Nov 18, 2024
7. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
8. Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
9. Chat with your tabular data in www.techtonique.net Oct 30, 2024
10. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
11. R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
12. R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
13. Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
14. Gradient-Boosting anything (alert: high performance) Oct 6, 2024
15. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
16. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
17. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
18. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
19. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
20. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
21. New versions of nnetsauce and ahead Sep 9, 2024
22. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
23. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
24. R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
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26. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
27. Bayesian (nonlinear) adaptive learning Aug 12, 2024
28. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
29. Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
30. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
31. learningmachine for Python (new version) Jul 15, 2024
32. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
33. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
34. 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
35. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
36. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
37. Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
38. Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
39. Forecasting the Economy May 27, 2024
40. A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
41. Probability of receiving a loan; using learningmachine May 12, 2024
42. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
43. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
44. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
45. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
46. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
47. learningmachine v1.1.2: for Python Apr 1, 2024
48. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
49. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
50. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
51. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
52. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
53. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
54. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
55. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
56. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
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58. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
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60. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
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62. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
63. Diffusion models in Python with esgtoolkit Dec 11, 2023
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68. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
69. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
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75. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
76. Forecasting in Python with ahead Sep 11, 2023
77. Risk-neutralize simulations Sep 4, 2023
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91. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
92. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
93. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
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95. Tuning and interpreting LSBoost Nov 15, 2021
96. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
97. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
98. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
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100. Classification using linear regression Sep 26, 2021
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102. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
103. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
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108. Bayesian Optimization with GPopt Apr 16, 2021
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110. Explaining xgboost predictions with the teller Mar 12, 2021
111. An infinity of time series models in nnetsauce Mar 6, 2021
112. New activation functions in mlsauce's LSBoost Feb 12, 2021
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170. Model calibration with `crossval` Oct 4, 2019
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177. crossval Mar 13, 2019
178. test Mar 10, 2019