Data Augmentation for Multivariate Time Series Classification: An Experimental Study
This work addresses data scarcity for researchers and practitioners in time series analysis, but it is incremental as it adapts existing methods to a specific domain.
The study tackled the problem of data scarcity in multivariate time series classification by investigating data augmentation techniques, achieving classification accuracy improvements in 10 out of 13 datasets using Rocket and InceptionTime models.
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.