Review of Data-centric Time Series Analysis from Sample, Feature, and Period

• URL: http://arxiv.org/abs/2404.16886v1
• Date: Wed, 24 Apr 2024 00:34:44 GMT
• Title: Review of Data-centric Time Series Analysis from Sample, Feature, and Period
• Authors: Chenxi Sun, Hongyan Li, Yaliang Li, Shenda Hong,
• Abstract summary: A good time-series dataset is advantageous for the model's accuracy, robustness, and convergence. The emergence of data-centric AI represents a shift in the landscape from model refinement to prioritizing data quality. We systematically review different data-centric methods in time series analysis, covering a wide range of research topics.
• Score: 37.33135447969283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data is essential to performing time series analysis utilizing machine learning approaches, whether for classic models or today's large language models. A good time-series dataset is advantageous for the model's accuracy, robustness, and convergence, as well as task outcomes and costs. The emergence of data-centric AI represents a shift in the landscape from model refinement to prioritizing data quality. Even though time-series data processing methods frequently come up in a wide range of research fields, it hasn't been well investigated as a specific topic. To fill the gap, in this paper, we systematically review different data-centric methods in time series analysis, covering a wide range of research topics. Based on the time-series data characteristics at sample, feature, and period, we propose a taxonomy for the reviewed data selection methods. In addition to discussing and summarizing their characteristics, benefits, and drawbacks targeting time-series data, we also introduce the challenges and opportunities by proposing recommendations, open problems, and possible research topics.

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