NEWS

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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