Open AccessSupercomputer power consumption prediction using machine learning, nonlinear algorithms, and statistical methods
Author(s) -
Jiří Tomčala
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2090/1/012087
Subject(s) - computer science , machine learning , preprocessor , power consumption , supercomputer , nonlinear system , data pre processing , algorithm , time series , series (stratigraphy) , artificial intelligence , work (physics) , power (physics) , data mining , parallel computing , engineering , mechanical engineering , paleontology , physics , quantum mechanics , biology
This work describes various methods of time series prediction. It illustrates the differences between machine learning methods, nonlinear algorithms, and statistical methods in their approach to prediction, and tries to explain in depth the principles of some of the most widely used representatives of these types of prediction methods. All of these methods are then tested on a time series from the real world: the course of power consumption of a supercomputer infrastructure. The reader is gradually acquainted with data analysis, preprocessing, the principle of the methods, and finally with the prediction itself. The main benefit of the work is the final comparison of the results of this testing in terms of the accuracy of the predictions, and the time needed to calculate them.