Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series Classification (2312.03998v2)

Abstract: We argue that time series analysis is fundamentally different in nature to either vision or natural language processing with respect to the forms of meaningful self-supervised learning tasks that can be defined. Motivated by this insight, we introduce a novel approach called \textit{Series2Vec} for self-supervised representation learning. Unlike other self-supervised methods in time series, which carry the risk of positive sample variants being less similar to the anchor sample than series in the negative set, Series2Vec is trained to predict the similarity between two series in both temporal and spectral domains through a self-supervised task. Series2Vec relies primarily on the consistency of the unsupervised similarity step, rather than the intrinsic quality of the similarity measurement, without the need for hand-crafted data augmentation. To further enforce the network to learn similar representations for similar time series, we propose a novel approach that applies order-invariant attention to each representation within the batch during training. Our evaluation of Series2Vec on nine large real-world datasets, along with the UCR/UEA archive, shows enhanced performance compared to current state-of-the-art self-supervised techniques for time series. Additionally, our extensive experiments show that Series2Vec performs comparably with fully supervised training and offers high efficiency in datasets with limited-labeled data. Finally, we show that the fusion of Series2Vec with other representation learning models leads to enhanced performance for time series classification. Code and models are open-source at \url{https://github.com/Navidfoumani/Series2Vec.}

• The paper presents Series2Vec, a novel self-supervised method that learns representations from time series data using similarity-based tasks.
• It leverages order-invariant attention within a transformer framework to capture complex temporal dynamics without heavy feature engineering.
• Empirical results indicate that Series2Vec achieves competitive performance compared to supervised approaches, especially when labeled data is limited.

Introduction to Time Series Representation Learning

The analysis of time series data plays a critical role in various sectors, including health care, finance, and environmental monitoring. Time series datasets are characterized by sequential data points indexed in time order, often resulting in large amounts of data. Traditional machine learning approaches require extensively labeled data to achieve optimal performance, which can be costly and time-consuming to obtain. However, self-supervised learning, a branch of machine learning, offers a solution by learning insightful representations from data without the need for labeled information. One area where self-supervised learning has been less developed is time series analysis.

Self-Supervised Learning for Time Series

Unlike in vision and natural language processing, defining meaningful tasks for self-supervised learning in time series data has been challenging due to its inherent complexity. Traditional contrastive learning methods used for time series often risk creating positive sample variants that are less similar to the original data than the negative samples. To address these issues, a novel method named Series2Vec was introduced, which instead of focusing on data augmentation techniques, relies on similarity-based self-supervised tasks that utilize time series-specific measures to determine the target output for representation learning.

Series2Vec: A Novel Approach

Series2Vec uses a methodology that incorporates order-invariant attention to ensure that similar time series produce similar representations while preserving the uniqueness of differently behaving series. This technique is crucial since maintaining the temporal dynamics and structural characteristics of time series data is indispensable for a range of applications such as forecasting and anomaly detection.

The technique applies both to the time and frequency domains of the series, leveraging the transformer architecture's self-attention characteristic to compose representations that include information from similar time series within a batch. This not only streamlines the process of capturing complex time series relationships but bypasses the need for sophisticated feature engineering typically associated with these datasets. A critical insight behind Series2Vec is the focus on consistency in generating targets rather than the absolute accuracy of similarity measures, which has shown impressive performance in experiments.

Efficacy of Series2Vec

Results demonstrated that Series2Vec is competitive with fully supervised learning approaches, particularly when dealing with datasets that have limited labeled data. This indicates the potential of Series2Vec for use in scenarios where annotated data is scarce or expensive to acquire. Furthermore, when fused with other representation learning models, Series2Vec leads to notable performance improvements for time series classification tasks.

Conclusion and Future Work

Series2Vec represents a significant advancement in time series representation learning, outperforming existing self-supervised techniques while remaining robust in low-label scenarios. As a final contribution, it has been shown that Series2Vec can act as a complementary strategy alongside other self-supervised learning methods to further augment classification performance.

The code and models associated with the Series2Vec are openly available, facilitating future research and practical deployment within diverse real-world applications. The effectiveness of Series2Vec highlights the value of representation learning in time series analysis and opens doors for further exploration of this self-supervised learning paradigm.