Package: flowz
Type: Package
Title: Local Latent Flow Forecasting with Empirical Calibration
Version: 0.1.0
Authors@R: c(
    person(
      "Giancarlo", "Vercellino",
      email = "giancarlo.vercellino@gmail.com",
      role = c("aut", "cre")
    )
  )
Description: 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>.
URL: https://rpubs.com/giancarlo_vercellino/flowz
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: forecast, fracdiff, graphics, grDevices, parallel, stats,
        utils
Suggests: testthat (>= 3.0.0), pkgload
Config/testthat/edition: 3
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-10-19 14:24:55 UTC; root
Author: Giancarlo Vercellino [aut, cre]
Maintainer: Giancarlo Vercellino <giancarlo.vercellino@gmail.com>
Repository: https://pigian.r-universe.dev
Date/Publication: 2026-10-18 16:20:21 UTC
RemoteUrl: https://github.com/cran/flowz
RemoteRef: HEAD
RemoteSha: 2cdbc4a06b68fc94c9350c149611e6db0cc0635a