Survey on Feature Representation and Similarity Measurement of Time Series
Jisuanji kexue yu tansuo, 2021 Time series is a group of random numbers which are composed of the values of the same index according to the time sequence. With the rapid development of science and technology, the application of time series in the field of data mining becomes more and ...
SUN Dongpu, QU Li
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Time-series representation learning via Time-Frequency Fusion Contrasting [PDF]
Frontiers in Artificial IntelligenceTime series is a typical data type in numerous domains; however, labeling large amounts of time series data can be costly and time-consuming. Learning effective representation from unlabeled time series data is a challenging task.
Wenbo Zhao, Ling Fan
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Characteristic Representation of Stock Time Series Based on Trend Feature Points [PDF]
IEEE Access, 2020 Stocks are the most active part of the securities market, and the analysis of stock generally starts from the price fluctuation. Stock trading data have the characteristics of time series, which make it possible to record the transaction prices in a time-
Mengna Zhou +3 more
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Time-Series Representation Feature Refinement with a Learnable Masking Augmentation Framework in Contrastive Learning [PDF]
SensorsIn this study, we propose a novel framework for time-series representation learning that integrates a learnable masking-augmentation strategy into a contrastive learning framework.
Junyeop Lee +3 more
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DABaCLT: A Data Augmentation Bias-Aware Contrastive Learning Framework for Time Series Representation [PDF]
Applied Sciences, 2023 Contrastive learning, as an unsupervised technique, has emerged as a prominent method in time series representation learning tasks, serving as a viable solution to the scarcity of annotated data.
Yubo Zheng +4 more
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Adaptive law-based feature representation for time series classification [PDF]
Scientific ReportsTime series classification (TSC) underpins applications across finance, healthcare, and environmental monitoring, yet real-world series often contain noise, local misalignment, and multiscale patterns. We introduce adaptive law-based transformation (ALT),
Marcell T. Kurbucz +4 more
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Stock Embeddings: Representation Learning for Financial Time Series
Engineering Proceedings, 2023 Identifying meaningful and actionable relationships between the price movements of financial assets is a challenging but important problem for many financial tasks, from portfolio optimization to sector classification.
Rian Dolphin, Barry Smyth, Ruihai Dong
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Distance- and Momentum-Based Symbolic Aggregate Approximation for Highly Imbalanced Classification
Sensors, 2022 Time-series representation is the most important task in time-series analysis. One of the most widely employed time-series representation method is symbolic aggregate approximation (SAX), which converts the results from piecewise aggregate approximation ...
Dong-Hyuk Yang, Yong-Shin Kang
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Time series classification through visual pattern recognition
Journal of King Saud University: Computer and Information Sciences, 2022 In this paper, a new approach to time series classification is proposed. It transforms the scalar time series into a two-dimensional space of amplitude (time series values) and a change of amplitude (increment).
Agnieszka Jastrzebska
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Visualising deep network time-series representations
Neural Computing and Applications, 2021 Comment: Accepted to Neural Computing and ...
Błażej Leporowski +1 more
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