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

TL;DR

This paper systematically reviews data-centric methods in time series analysis, emphasizing the importance of data quality at sample, feature, and period levels, and proposes a taxonomy, challenges, and future research directions.

Contribution

It provides a comprehensive taxonomy and analysis of data-centric approaches in time series analysis, addressing a research gap and suggesting future directions.

Findings

Proposes a taxonomy based on data characteristics at sample, feature, and period levels.

Summarizes benefits and drawbacks of various data-centric methods.

Identifies open problems and opportunities for future research.

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.