Package: flowz 0.1.0

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>.

Authors:Giancarlo Vercellino [aut, cre]