NEWS

---

ahead 0.37.0

Date: 2026-05-08

• Add condvolf

ahead 0.36.0

• Add sensitivity analysis to ahead::dynrmf

ahead 0.32.0

• Add contextridge2f for adding context to ridge2f forecasting.
• Add stackeridge2f for time series stacked generalization

ahead 0.25.0

• Implement ANY MODEL+GARCH(1, 1) forecasting for stocks (stochastic volatility models)
• armagarchf doesn't use the bootstrap by default anymore

ahead 0.24.0

• add fitted values in ridge2f

ahead 0.22.0

• add explicit clustering to volatility forecasting in mlarch

ahead 0.18.0

• Add ML-ARCH model, see vignettes for more examples

ahead 0.17.0

• Add conformal prediction to ridge2f (with KDE, bootstrap, block bootstrap and sequential split conformal prediction)
• Faster install, less imports

ahead 0.14.0

• Add fit_func and predict_func for custom fitting and prediction functions of ahead::dynrmf (using caret Machine Learning).
• Add forecasting combinations based on ForecastComb, adding Ridge and Elastic Net to the mix.

ahead 0.11.0

• Include tests (90% coverage). After cloning, run:

install.packages("covr")
covr::report()

ahead 0.10.0

• Univariate forecasting for ridge2f. See https://thierrymoudiki.github.io/blog/2024/02/26/python/r/julia/ahead-v0100.
• Fast calibration for ridge2f (univariate and multivariate case). See https://thierrymoudiki.github.io/blog/2024/02/26/python/r/julia/ahead-v0100.

ahead 0.9.0

• progress bars for bootstrap (independent, circular block, moving block)

ahead 0.8.0

• empirical marginals for R-Vine copula simulation
• risk-neutralize simulations

ahead 0.7.0

• moving block bootstrap in ridge2f, basicf and loessf, in addition to circular block bootstrap from 0.6.2
• adjust R-Vine copulas on residuals for ridge2f simulation
• new plots for simulations see (new) vignettes
• split conformal prediction intervals (very very experimental and basic right now, too conservative)
• Depends and selective Imports (beneficial to Python and rpy2 for installation time?)
• getsimulations extracts simulations from a given time series (from ridge2f and basicf)
• getreturns extracts returns/log-returns from multivariate time series
• splitts splits time series using a proportion of data

ahead 0.6.2

• Add Block Bootstrap to ridge2f
• Add external regressors to ridge2f
• Add clustering to ridge2f
• Add Block Bootstrap to loessf
• Create new vignettes for ridge2f and loessf

ahead 0.6.1

• Align version with Python's
• Temporarily remove dependency with cclust

ahead 0.6.0

• Include basic methods: mean forecast, median forecast, random walk forecast

ahead 0.5.0

• add dropout regularization to ridge2f
• parallel execution for type_pi == bootstrap in ridge2f (done in R /!, experimental)
• preallocate matrices for type_forecast == recursive in ridge2f

ahead 0.4.2

• new attributes mean, lower bound, upper bound forecast as numpy arrays

ahead 0.4.1

• use get_frequency to get series frequency as a number
• create a function get_tscv_indices for getting time series cross-validation indices