While there are many computer programs aimed to resolve this problem,  they often fail to meet expectations of a novice user at least in the following ways:

• Tightly specialized applications can effectively forecast only a narrow set of typical situations. The nuisance is, situations tend to change swiftly.
• Diverse statistical analyzers require at least a moderate knowledge of the algorithms they expose. Such knowledge costs time and money.
• Powerful and flexible neuronets appear quite dumb on a short data series. In fact, additional data happen to be expensive, if available at all.

To consolidate and mutually reinforce the above approaches we suggest the universal shell built up as an expert system and aimed to combine the most popular forecasting algorithms in the automated competitive environment.

Given a multidimensional time series, the system automatically hypothesizes on a set of all available solutions seeking to minimize the aggregate difference between backforecasts and the actual values of the supplied series. The most effective hypotheses then integrate into the final model which, in turn, is used to predict future responses.