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flowz
View on CRAN: Click
here
Download and install flowz package within the R console
Install from CRAN:
install.packages("flowz")
Install from Github:
library("remotes")
install_github("cran/flowz") Install by package version:
library("remotes")
install_version("flowz", "0.1.0") Attach the package and use:
library("flowz")
Maintained by
Giancarlo Vercellino
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-10-18
Latest Update: 2026-10-18
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>.
How to cite:
Giancarlo Vercellino (2026). flowz: Local Latent Flow Forecasting with Empirical Calibration. R package version 0.1.0, https://cran.r-project.org/web/packages/flowz. Accessed 25 Oct. 2026.
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