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pro vyhledávání: '"Nochumsohn, Liran"'
Time series forecasting is critical in numerous real-world applications, requiring accurate predictions of future values based on observed patterns. While traditional forecasting techniques work well in in-domain scenarios with ample data, they strug
Externí odkaz:
http://arxiv.org/abs/2411.15743
Autor:
Nochumsohn, Liran, Azencot, Omri
Data augmentation serves as a popular regularization technique to combat overfitting challenges in neural networks. While automatic augmentation has demonstrated success in image classification tasks, its application to time-series problems, particul
Externí odkaz:
http://arxiv.org/abs/2405.00319