flowz: Local Latent Flow Forecasting with Empirical Calibration

Fits conditional flow-matching models to univariate time series. Combines a selectable exponentially weighted moving average (EWMA), autoregressive integrated moving average (ARIMA), autoregressive fractionally integrated moving average (ARFIMA), or structural time-series baseline with direct multi-step residual paths and a random-feature extreme learning machine (ELM) velocity field. Spectral dispersion calibration uses a chronological calibration block, with a separate test block for forecast scoring. Provides simulated forecast paths and smooth marginal distributions. Methodological background is given by Lipman et al. (2023) <doi:10.48550/arXiv.2210.02747>, Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, Hyndman and Khandakar (2008) <doi:10.18637/jss.v027.i03>, and Haslett and Raftery (1989) <doi:10.2307/2347679>.

Version:	0.1.0
Depends:	R (≥ 4.1.0)
Imports:	forecast, fracdiff, graphics, grDevices, parallel, stats, utils
Suggests:	testthat (≥ 3.0.0), pkgload
Published:	2026-10-18
DOI:	10.32614/CRAN.package.flowz
Author:	Giancarlo Vercellino [aut, cre]
Maintainer:	Giancarlo Vercellino <giancarlo.vercellino at gmail.com>
License:	GPL-3
URL:	https://rpubs.com/giancarlo_vercellino/flowz
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	flowz results

Documentation:

Reference manual:	flowz.html , flowz.pdf
Vignettes:	Introduction to Local Latent Flow Forecasting (source, R code)

Downloads:

Package source:	flowz_0.1.0.tar.gz
Windows binaries:	r-devel: flowz_0.1.0.zip, r-release: flowz_0.1.0.zip, r-oldrel: flowz_0.1.0.zip
macOS binaries:	r-release (arm64): flowz_0.1.0.tgz, r-oldrel (arm64): flowz_0.1.0.tgz, r-release (x86_64): flowz_0.1.0.tgz, r-oldrel (x86_64): flowz_0.1.0.tgz

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