Method for the prediction of some time series using small sets of experimental samples
The paper cares with the tactic of prediction of your time series supported the concepts of system identification. The distinctive property of the tactic is that the use of small sets of experimental samples of knowledge . The latter create some basis for building so-called learning subsets, which are wont to construct particular prediction models. Values of variables predicted by different particular models allow calculating the specified variables by employing a batch voting technique. the tactic are often used for short-term prediction of knowledge values at future time instants supported the analysis of a quick history of the method into account . It are often useful in cases of processing very large arrays of knowledge samples, when the researcher has got to confirm his (her) attention to only alittle a part of the samples received at the last instants of your time , in sight of the limited memory of the pc or in cases when very slow processes are analyzed. A special place within the paper is given to the instance during which the computational aspects of the proposed method are considered intimately .