TS2Vec: Towards Universal Representation of Time Series (2106.10466v4)

Abstract: This paper presents TS2Vec, a universal framework for learning representations of time series in an arbitrary semantic level. Unlike existing methods, TS2Vec performs contrastive learning in a hierarchical way over augmented context views, which enables a robust contextual representation for each timestamp. Furthermore, to obtain the representation of an arbitrary sub-sequence in the time series, we can apply a simple aggregation over the representations of corresponding timestamps. We conduct extensive experiments on time series classification tasks to evaluate the quality of time series representations. As a result, TS2Vec achieves significant improvement over existing SOTAs of unsupervised time series representation on 125 UCR datasets and 29 UEA datasets. The learned timestamp-level representations also achieve superior results in time series forecasting and anomaly detection tasks. A linear regression trained on top of the learned representations outperforms previous SOTAs of time series forecasting. Furthermore, we present a simple way to apply the learned representations for unsupervised anomaly detection, which establishes SOTA results in the literature. The source code is publicly available at https://github.com/yuezhihan/ts2vec.

An Overview of TS2Vec: Universal Representation of Time Series

The paper "TS2Vec: Towards Universal Representation of Time Series" introduces TS2Vec, a framework designed to learn universal representations of time series data. The framework applies contrastive learning in a hierarchical manner over augmented context views, effectively generating robust contextual representations at various granularity levels. The approach aims to improve the current state of time series representation, particularly in unsupervised learning contexts.

Methodology

TS2Vec differentiates itself by utilizing hierarchical contrasting to capture multi-scale contextual information. The model is trained using both instance-wise and temporal contrastive losses, enabling the encoding of time series dynamics across different semantic levels. Key elements of the architecture include:

• Encoder Architecture: It involves an input projection layer, a timestamp masking module, and a dilated CNN module. The projection layer transforms input data into a higher-dimensional latent space, while the CNN module effectively captures long-range dependencies using dilated convolutions.
• Contrastive Learning: TS2Vec employs two types of contrasting – temporal, which ensures smoothness over time by using consistent representation across timestamps, and instance-wise, which differentiates between time series examples.
• Contextual Consistency: Positive pairs are constructed by ensuring that the same timestamp's representation is consistent across different augmented views, generated by timestamp masking and random cropping. This approach reduces the likelihood of bias often introduced by traditional data augmentations.

Experimental Evaluation

The paper presents extensive evaluations of TS2Vec across various tasks:

• Time Series Classification: The method achieves significant accuracy improvements over state-of-the-art unsupervised methods on UCR and UEA datasets. This includes a noted 2.4% and 3.0% increase in accuracy on UCR and UEA datasets, respectively.
• Time Series Forecasting: TS2Vec demonstrates superior performance in both univariate and multivariate settings, achieving an average MSE reduction of 32.6% and 28.2% compared to Informer, LogTrans, and other baselines. The retained efficiency, even with variants in prediction horizons, highlights the versatility of the learned representations.
• Anomaly Detection: The framework sets new benchmarks on Yahoo and KPI datasets, emphasizing its capability to identify anomalies effectively without a dependence on dataset-specific training.

Implications and Future Directions

The improvements achieved by TS2Vec have strong implications for real-world applications involving complex time series data, including finance, climate modeling, and bioinformatics. The framework’s robustness to missing data and computational efficiency further underscore its practicality for industrial scale time series analysis.

The paper points toward several future directions, including the extension of TS2Vec to handle more complex data structures beyond time series. Additional insights could be garnered by exploring its applicability to real-time analysis and adaptive learning scenarios, thereby enhancing the model’s scalability and adaptability in dynamic environments.

TS2Vec represents a significant step forward in addressing the challenges of learning universal representations for time series, offering a flexible and comprehensive framework that marries theoretical advancement with practical applicability.