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Probabilistic Time Series Forecasting with XGBoost and Conformal Inference

v1.2 · Mar 29, 2026 · GPL-3

Description

Implements a probabilistic approach to time series forecasting combining XGBoost regression with conformal inference methods. The package provides functionality for generating predictive distributions, evaluating uncertainty, and optimizing hyperparameters using Bayesian, coarse-to-fine, or random search strategies.

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r-devel-linux-x86_64-debian-clang OK
r-devel-linux-x86_64-debian-gcc OK
r-devel-linux-x86_64-fedora-clang OK
r-devel-linux-x86_64-fedora-gcc OK
r-devel-windows-x86_64 OK
r-oldrel-macos-arm64 OK
r-oldrel-macos-x86_64 OK
r-oldrel-windows-x86_64 OK
r-patched-linux-x86_64 OK
r-release-linux-x86_64 OK
r-release-macos-arm64 OK
r-release-macos-x86_64 OK
r-release-windows-x86_64 OK

Check History

OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies normalp glogis gld purrr ald evd GeneralizedHyperbolic cubature furrr future xgboost rBayesianOptimization lubridate ggplot2 scales xpect

Version History

4 tracked
updated 1.2 ← 1.1 diff Mar 29, 2026
new 1.1 Mar 10, 2026
updated 1.1 ← 1.0 diff Feb 13, 2026
new 1.0 Mar 23, 2025