Review of Data-centric Time Series Analysis from Sample, Feature, and Period
<|reference_start|>Review of Data-centric Time Series Analysis from Sample, Feature, and Period: 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.<|reference_end|>
@article{sun2024review,
    title={Review of Data-centric Time Series Analysis from Sample, Feature, and
  Period},
    author={Chenxi Sun, Hongyan Li, Yaliang Li, Shenda Hong},
    journal={arXiv preprint arXiv:2404.16886},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16886},
    primaryClass={cs.LG cs.AI}
}