Universal Time-Series Representation Learning: A Survey
Patara Trirat Yooju Shin Junhyeok Kang Youngeun Nam Jihye Na Minyoung Bae Joeun Kim Byunghyun Kim Jae-Gil Lee
Time-series data exists in every corner of real-world systems and services,
ranging from satellites in the sky to wearable devices on human bodies.
Learning representations by extracting and inferring valuable information from
these time series is crucial for understanding the complex dynamics of
particular phenomena and enabling informed decisions. With the learned
representations, we can perform numerous downstream analyses more effectively.
Among several approaches, deep learning has demonstrated remarkable performance
in extracting hidden patterns and features from time-series data without manual
feature engineering. This survey first presents a novel taxonomy based on three
fundamental elements in designing state-of-the-art universal representation
learning methods for time series. According to the proposed taxonomy, we
comprehensively review existing studies and discuss their intuitions and
insights into how these methods enhance the quality of learned representations.
Finally, as a guideline for future studies, we summarize commonly used
experimental setups and datasets and discuss several promising research
directions. An up-to-date corresponding resource is available at
https://github.com/itouchz/awesome-deep-time-series-representations.