Embarrassingly Simple MixUp for Time-series

Abstract: Labeling time series data is an expensive task because of domain expertise and dynamic nature of the data. Hence, we often have to deal with limited labeled data settings. Data augmentation techniques have been successfully deployed in domains like computer vision to exploit the use of existing labeled data. We adapt one of the most commonly used technique called MixUp, in the time series domain. Our proposed, MixUp++ and LatentMixUp++, use simple modifications to perform interpolation in raw time series and classification model's latent space, respectively. We also extend these methods with semi-supervised learning to exploit unlabeled data. We observe significant improvements of 1\% - 15\% on time series classification on two public datasets, for both low labeled data as well as high labeled data regimes, with LatentMixUp++.

• The paper demonstrates that adapting MixUp for time-series with MixUp++ and LatentMixUp++ improves model robustness and classification performance.
• It introduces interpolating in both raw and latent spaces, ensuring semantic consistency and better alignment with real-world data distributions.
• Empirical results on UCI HAR and Sleep-EDF datasets show performance gains of 1% to 15% and enhanced outcomes with pseudo-labeling in low-data regimes.

Embarrassingly Simple MixUp for Time-series

Introduction

The paper "Embarrassingly Simple MixUp for Time-series" (2304.04271) addresses the challenge of labeling time series data, which is notably resource-intensive due to its temporal dynamics and domain specificity. The scarcity of labeled data, particularly in high-stakes domains such as healthcare, necessitates efficient data augmentation techniques. While MixUp, a data augmentation method initially proposed for computer vision, has shown promise in other domains, this paper adapts and extends the technique for time-series data through MixUp++ and LatentMixUp++.

Methodology

The original MixUp method involves linear interpolation between data points to generate synthetic samples. This method, effective in the image domain, faces challenges in time series due to potential semantic inconsistencies when interpolating in the raw data space. To mitigate these issues, the paper introduces two adaptations:

1. MixUp++: This variant combines original data retention with multiple MixUp operations per data batch. By preserving the original data, the method ensures the model's decision boundary aligns better with real-world data distributions.
2. LatentMixUp++: By performing interpolations within the latent space of a model, this approach aims to capture semantically meaningful interpolations, benefiting from the more linear characteristic of neural network embeddings.

Additionally, the study extends these methods to semi-supervised settings through pseudo-labeling. Unlabeled data is pseudolabeled using high-confidence predictions from the model, integrating these synthetic labels into the augmented training process.

Empirical Results

The effectiveness of MixUp++ and LatentMixUp++ was evaluated on two public datasets: UCI HAR (Human Activity Recognition) and Sleep-EDF. Results demonstrate significant classification improvements across various metrics such as Accuracy, F1 Macro, and Cohen's Kappa, with LatentMixUp++ exhibiting the most pronounced gains, particularly in low-labeled data regimes. Notably, LatentMixUp++ consistently outperformed the original MixUp and other augmentation baselines by 1% to 15% in performance metrics.

Furthermore, in the semi-supervised setting, the combination of pseudo-labeling with MixUp resulted in notable performance enhancements, especially in circumstances with very limited labeled data.

Implications and Future Directions

The proposed MixUp++ and LatentMixUp++ methodologies present simple yet effective solutions to the challenges of limited time series data in machine learning applications. By efficiently utilizing both labeled and unlabeled data, these methods enhance model robustness and generalization. Future work could explore further refinements or adaptations of these methods across diverse time-series domains, as well as their integration with other semi-supervised learning frameworks. Moreover, investigating the specific impact of interpolations in the latent space on model interpretability and feature importance could yield deeper insights into the underlying mechanisms of the proposed methods.

Conclusion

The paper provides a compelling demonstration of how simple adaptations of an existing augmentation method can yield substantial benefits in a challenging domain like time series classification. By utilizing MixUp++ and LatentMixUp++, alongside pseudo-labeling, the research offers a practical and scalable approach to improving model performance in scenarios with limited labeled data, thereby potentially broadening the applicability of machine learning in diverse real-world time-series analyses.