Our study investigates the impact of data augmentation on the performance of multivariate time series models, focusing on datasets from the UCR archive. Despite the limited size of these datasets, we achieved classification accuracy improvements in 10 out of 13 datasets using the Rocket and InceptionTime models. This highlights the essential role of sufficient data in training effective models, paralleling the advancements seen in computer vision. Our work delves into adapting and applying existing methods in innovative ways to the domain of multivariate time series classification. Our comprehensive exploration of these techniques sets a new standard for addressing data scarcity in time series analysis, emphasizing that diverse augmentation strategies are crucial for unlocking the potential of both traditional and deep learning models. Moreover, by meticulously analyzing and applying a variety of augmentation techniques, we demonstrate that strategic data enrichment can enhance model accuracy. This not only establishes a benchmark for future research in time series analysis but also underscores the importance of adopting varied augmentation approaches to improve model performance in the face of limited data availability.

翻译：本研究探讨了数据增强对多元时间序列模型性能的影响，重点关注UCR存档中的数据集。尽管这些数据集规模有限，但通过使用Rocket和InceptionTime模型，我们在13个数据集中有10个实现了分类准确率的提升。这凸显了充足数据在训练有效模型中的关键作用，与计算机视觉领域的进展相呼应。我们的工作深入探索了将现有方法以创新方式应用于多元时间序列分类领域。对这些技术的全面研究为应对时间序列分析中的数据稀缺问题设立了新标准，强调多样化的增强策略对于释放传统模型和深度学习模型潜力至关重要。此外，通过细致分析并应用多种增强技术，我们证明战略性数据富集能够提升模型准确率。这不仅为时间序列分析的未来研究建立了基准，也强调了在数据有限的情况下，采用多样化增强方法对于改善模型性能的重要性。