Supercomputer power consumption prediction using machine learning, nonlinear algorithms, and statistical methods
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...
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| Published in | Journal of physics. Conference series Vol. 2090; no. 1; pp. 12087 - 12108 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
IOP Publishing
01.11.2021
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| Online Access | Get full text |
| ISSN | 1742-6588 1742-6596 1742-6596 |
| DOI | 10.1088/1742-6596/2090/1/012087 |
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| Summary: | 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. |
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| ISSN: | 1742-6588 1742-6596 1742-6596 |
| DOI: | 10.1088/1742-6596/2090/1/012087 |