Overview of Data Augmentation Techniques in Time Series Analysis
This paper explores the transformative impact of data augmentation on time series analysis, highlighting classic methods such as interpolation and advanced techniques like GANs and VAEs. It examines the challenges, including computational costs and risks of overfitting, while showcasing the advantages of these methods. Offering a balanced perspective, the paper serves as a comprehensive guide for researchers and practitioners to address challenges and drive advancements in machine learning and deep learning applications for time series data.
1 janv. 2024
