Review of Data-centric Time Series Analysis from Sample, Feature, and Period
CoRR(2024)
摘要
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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