% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lag_handling.R
\name{unroll_stream}
\alias{unroll_stream}
\title{Unroll a stream-wise quantity back to the original time scale}
\usage{
unroll_stream(stream_vals, k, h, T_)
}
\arguments{
\item{stream_vals}{Numeric vector. Values for stream k (length ~ T/h).}

\item{k}{Integer. Stream index (1-based).}

\item{h}{Integer. Lag.}

\item{T_}{Integer. Total original sequence length.}
}
\value{
Numeric vector of length T_.
}
\description{
After computing a per-stream cumulative quantity (e.g. e-process values),
restores them to the original length \code{T} by zero-padding the first
\code{k - 1} positions, repeating each stream value \code{h} times, then truncating
to length \code{T}.
}
\section{Alignment only, not theoretical updating}{

For lagged forecasts (\code{h >= 2}), the returned series is aligned to the
evaluated score-difference index after stream splitting. It should
\strong{not} be interpreted as a process that updates at the original
forecast-issuance time. The unrolled process is for visualization and
alignment only; the theoretical validity argument relies strictly on the
streamwise sub-filtrations, not on this unrolled representation.
}

\examples{
unroll_stream(c(1, 2, 3), k = 2, h = 2, T_ = 6)

}
