

    \filetitle{rmse}{Compute RMSE for given observations and predictions}{tseries/rmse}

	\paragraph{Syntax}

\begin{verbatim}
[Rmse,Pe] = rmse(Obs,Pred)
[Rmse,Pe] = rmse(Obs,Pred,Range,...)
\end{verbatim}

\paragraph{Input arguments}

\begin{itemize}
\item
  \texttt{Obs} {[} tseries {]} - Input data with observations.
\item
  \texttt{Pred} {[} tseries {]} - Input data with predictions (a
  different prediction horizon in each column); \texttt{Pred} is
  typically the outcome of the Kalman filter,
  \href{model/filter}{\texttt{model/filter}} or
  \href{VAR/filter}{\texttt{VAR/filter}}, called with the option
  \texttt{'ahead='}.
\item
  \texttt{Range} {[} numeric \textbar{} \texttt{Inf} {]} - Date range on
  which the RMSEs will be evaluated; \texttt{Inf} means the entire
  possible range available.
\end{itemize}

\paragraph{Output arguments}

\begin{itemize}
\item
  \texttt{Rmse} {[} numeric {]} - Numeric array with RMSEs for each
  column of \texttt{Pred}.
\item
  \texttt{Pe} {[} tseries {]} - Prediction errors, i.e.~the difference
  \texttt{Obs - Pred} evaluated within \texttt{Range}.
\end{itemize}

\paragraph{Description}

\paragraph{Example}


