  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.
