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

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.