Review of Data-centric Time Series Analysis from Sample, Feature, and Period
This review provides a structured overview for researchers and practitioners in time series analysis, but it is incremental as it synthesizes existing methods without introducing new techniques.
The paper systematically reviews data-centric methods in time series analysis, proposing a taxonomy based on sample, feature, and period characteristics to address the gap in research on data quality prioritization over model refinement.
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.