L-GTA: Latent Generative Modeling for Time Series Augmentation
This addresses data scarcity in time series analysis for tasks like forecasting and classification, though it appears incremental as it builds on existing generative and transformation techniques.
The paper tackled the problem of generating synthetic time series data for augmentation by introducing L-GTA, a latent generative model using transformers, which resulted in significant improvements in predictive accuracy and similarity measures compared to direct methods.
Data augmentation is gaining importance across various aspects of time series analysis, from forecasting to classification and anomaly detection tasks. We introduce the Latent Generative Transformer Augmentation (L-GTA) model, a generative approach using a transformer-based variational recurrent autoencoder. This model uses controlled transformations within the latent space of the model to generate new time series that preserve the intrinsic properties of the original dataset. L-GTA enables the application of diverse transformations, ranging from simple jittering to magnitude warping, and combining these basic transformations to generate more complex synthetic time series datasets. Our evaluation of several real-world datasets demonstrates the ability of L-GTA to produce more reliable, consistent, and controllable augmented data. This translates into significant improvements in predictive accuracy and similarity measures compared to direct transformation methods.