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# CITATION.cff file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "flowz" in publications use:'
type: software
license: GPL-3.0-only
title: 'flowz: Local Latent Flow Forecasting with Empirical Calibration'
version: 0.1.0
doi: 10.32614/CRAN.package.flowz
abstract: 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) <https://doi.org/10.48550/arXiv.2210.02747>,
  Huang et al. (2006) <https://doi.org/10.1016/j.neucom.2005.12.126>, Hyndman and
  Khandakar (2008) <https://doi.org/10.18637/jss.v027.i03>, and Haslett and Raftery
  (1989) <https://doi.org/10.2307/2347679>.
authors:
- family-names: Vercellino
  given-names: Giancarlo
  email: giancarlo.vercellino@gmail.com
repository: https://pigian.r-universe.dev
commit: 2cdbc4a06b68fc94c9350c149611e6db0cc0635a
url: https://rpubs.com/giancarlo_vercellino/flowz
date-released: '2026-10-18'
contact:
- family-names: Vercellino
  given-names: Giancarlo
  email: giancarlo.vercellino@gmail.com