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Regression ensembles without mlr3pipelines1 months ago
A smaller runtime | A runnable example | What is learned, and where | Migration and reproducibility
naive v2: dependency-free empirical extrapolation1 months ago
Getting started with segen1 months ago
Numeric example | Reproducibility | Distance methods and parallel execution | Changes in 2.0.1 | Input requirements
Getting started with proteus1 months ago
A first forecast | Optional features
Getting started with spooky 2.01 months ago
Overview | Numeric forecasting | Categorical forecasting | Reproducibility
lambdaTS 2.0: probabilistic multivariate forecasting1 months ago
Overview | Forecasting | Reproducibility
Intro to rollcast1 months ago
What you can do with rollcast | 1) Fit rollcast, aka anchor first, argue later | 2) Peek at the anchor mix | 3) Forecast paths, not just a line | 4) Try fixed mode versus search mode | 5) Residuals: pure anchors or local texture | 6) Minimal plots, tiny but telling | Conclusion
Intro to CRAFT2 months ago
CRAFTing future trajectories without pretending to own the future | The CRAFT package works this way | A tiny demo, because forecasts should have manners | 1. Fold the history into trajectories | 2. Fit a smooth distribution from trajectory draws | 3. Fit CRAFT end to end | 4. Predict again, because the future keeps moving | 5. What to look at when the model returns | Final thoughts