简介：Self-Supervised Contrastive Pre-Training for Time Series via Time-Frequency Transformation
Self-Supervised Contrastive Pre-Training for Time Series via Time-Frequency Transformation
In recent years, time series analysis has gained increasing attention in various fields due to its effectiveness in capturing sequential dependencies. However, the availability of labeled data is often limited, impeding the training of accurate models. To address this issue, we propose a self-supervised contrastive pre-training method for time series analysis, which utilizes time-frequency transformation to extract representative features.
Contrastive pre-training has shown promise in learning discriminative features without relying on labeled data. It pairs each input sample with a positive (similar) and negative (dissimilar) neighbor and teaches the model to distinguish between them. However, traditional contrastive methods often focus on pixel-level or patch-level relationships, which may not be suitable for time series data given its intrinsic temporal structure.
To overcome this limitation, we propose to apply time-frequency transformation to time series data prior to contrastive pre-training. Time-frequency transformation allows us to capture both global and local patterns of the time series effectively, providing a richer feature representation for the input. After transformation, we can apply contrastive pre-training on the resulting time-frequency representations to learn discriminative features.
We evaluated our method on several benchmark time series datasets, including rhythm recognition and gesture recognition. Experimental results demonstrate that our self-supervised contrastive pre-training method significantly outperforms state-of-the-art baselines, improving accuracy by up to 20%. We also compared the feature representation learned by our method with traditional pixel-level and patch-level contrastive methods, showing that our method学会的表示可以更好地 capture the temporal relationships in time series data.
These results suggest that our self-supervised contrastive pre-training method utilizing time-frequency transformation is effective in learning discriminative features for time series analysis, even without labeled data. Moreover, the learned feature representation可以实现好的 generalizability on unseen tasks,potentially facilitating future research in this area.
For future work, we plan to investigate more complex time-frequency transformations and their applications to diverse time series tasks, such as action recognition and weather forecasting. Additionally, we will explore the use of contrastive pre-training for domain adaptation and few-shot learning scenarios in time series analysis.
In conclusion, we have presented a self-supervised contrastive pre-training method for time series analysis that utilizes time-frequency transformation to extract representative features. Our method demonstrates promising results on benchmark datasets and highlights the potential of self-supervised learning for time series analysis with limited labeled data. We believe this work can serve as a valuable contribution to the field and inspire further research in self-supervised learning for time series analysis.