mlsanityData to 14 Oct 2026

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

arXiv 2404.16886 ↗First posted 24 Apr 2024cs.LG cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 67 references that are in the map. First posted on arXiv 24 Apr 2024.

Builds on

33 of 67 references.

References · 33

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21 more
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  1. 2025Optimizing the Training Diet: Data Mixture Search for Robust Time Series Forecastingcited by 0