Abstract
Accurate prediction methods are generally very computationally intensive, so they take a long time. Quick prediction methods, on the other hand, are not very accurate. Is it possible to design a prediction method that is both accurate and fast? In this paper, a new prediction method is proposed, based on the so-called random time-delay patterns, named the RTDP method. Using these random time-delay patterns, this method looks for the most important parts of the time series’ previous evolution, and uses them to predict its future development. When comparing the supercomputer infrastructure power consumption prediction with other commonly used prediction methods, this newly proposed RTDP method proved to be the most accurate and the second fastest.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
This paper presents a new nonlinear forecasting method that was designed to find the most significant parts of the previous time series evolution, and to produce forecasts very quickly, even for a seemingly chaotic time series
∑ δj, δj ∈ R {1, 2, . . . , δmax }, j =1 where y is the partial prediction, which is equal to the value of the predicted time series x following subsequence xkmin which is the subsequence that is most similar to xlast among all xk subsequences in the past, ε is the estimated error rate of this partial prediction, ε min is the distance norm between the last subsequence and the most similar subsequence, random time-delay pattern (RTDP) is a random time-delays pattern, m is the length of
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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