A Predictive Adaptive Learning Method for Multivariable Time Series With Mooney Viscosity Prediction as an Application Case
Time series prediction involves static and dynamic features. Extraneous input information hampers model performance, and the statistical attributes of time series change over time, affecting distribution. Targeted processing of input data for feature and distribution dynamics is vital. This article...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10433489/ |