A Long-Term Prediction Method of Computer Parameter Degradation Based on Curriculum Learning and Transfer Learning

Yuanhong Mao (), Zhong Ma, Xi Liu, Pengchao He and Bo Chai
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Yuanhong Mao: Xi’an Microelectronics Technology Institute, Xi’an 710065, China
Zhong Ma: Xi’an Microelectronics Technology Institute, Xi’an 710065, China
Xi Liu: Xi’an Microelectronics Technology Institute, Xi’an 710065, China
Pengchao He: Xi’an Microelectronics Technology Institute, Xi’an 710065, China
Bo Chai: Xi’an Microelectronics Technology Institute, Xi’an 710065, China

Mathematics, 2023, vol. 11, issue 14, 1-15

Abstract: The long-term prediction of the degradation of key computer parameters improves maintenance performance. Traditional prediction methods may suffer from cumulative errors in iterative prediction, which affect the model’s long-term prediction accuracy. Our network adopts curriculum learning and transfer learning methods, which can effectively solve this problem. The training network uses a dual-branch Siamese network. One branch intermixes the predicted and annotated data as input and uses curriculum learning to train. The other branch uses the original annotated data for training. To further align the hidden distributions of the two branches, the transfer learning method calculates the covariance matrices of the time series of the two branches by correlation alignment loss. A single branch is used in the test for prediction without increasing the inference computation. Compared with the current mainstream networks, our method can effectively improve the accuracy of long-term prediction with the improvements above.

Keywords: long-term prediction; time series; curriculum learning; transfer learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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