\paragraph{Threshold Determination} \label{threshold_determination}
It is assumed that a large number of incoming time series windows should be unclassifiable. This can only be determined
by specifying a threshold as upper bound for the distance between a time series window and its nearest neighbour. An
intuitive approach for the threshold determinations would include knowledge about the distances between the instances of
a class. This approach can not be tested in this experiment, because the recording instructions do have the great weakness
to contain only one instance for every class in the training data as later mentioned in section \ref{instructions_review}. The
following threshold determinations were tested.

\begin{itemize}
    \item \textbf{HMinD:} Stands for half minimum distance, this approach determines the threshold for a class by the
        half minimum distance of the class to all other classes in the training data.
    \item \textbf{HAveD:} Stands for half average distance, this approach determines the threshold for a class by the
        half average distance of the class to all other classes in the training data.
    \item \textbf{HMidD:} Stands for half middle distance, this approach determines the threshold for a class by the
        half average of the minimum and maximum distance of the class to all other classes in the training data.
    \item \textbf{Peak:} Stands for peaking, this approach fakes already generated knowledge about the distance
        threshold by setting the threshold to the by a small factor increased distance between the only existing
        training class instance and the test instance that has to be found. Two more intuitively chosen factors were
        tested, $\frac{11}{10}$ and $\frac{12}{10}$. The peaking approach exists only to judge the performance of other
        threshold determinations.
\end{itemize}
