values of weight coefficients with which patterns are complexed.
As method of optimization the comparison of variants by accuracy
criterion is used.
To select similar patterns from all possible patterns in the
time series, the following steps are developed:

*A. Reducing variable set size*

The choice of an optimal set of variables can be realised by
preselection. It is necessary to identify a subset of effective
variables, which were defined as the nucleus [17].

*B. Transformation of analogues*

As time-series may be non-stationary patterns with similar shapes
may have different mean values, standard deviations and trends.
In the literature, it is recommended to evaluate the difference
between the process and its trend which is an unknown function
of time. Another possibility gives the selection of differences
where the criterion of stationarity is used as selection criterion.

*C. Selection of the most similar analogues*

The closest analogue is founded as the first analogue A_{1},
the next one in distance A_{2} is called the second
analogue and so on to the last analogue A_{F}. Distances
can be measured by means of the Euclidean distance of points
of the output pattern and the analogue or by other measures
of distance.

*D. Combining forecasts*

Every selected analogue has its continuation which gives a forecast.
In such a way we obtain F forecasts, which are to combine. In
the literature there are several principles for combination
of forecasts, as weighted
sum for example [8,17,20].