Summary of Review Of Data-centric Time Series Analysis From Sample, Feature, and Period, by Chenxi Sun et al.
Review of Data-centric Time Series Analysis from Sample, Feature, and Period
by Chenxi Sun, Hongyan Li, Yaliang Li, Shenda Hong
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A systematic review of data-centric methods in time series analysis is presented, covering a wide range of research topics. The paper proposes a taxonomy for reviewed data selection methods based on time-series data characteristics at sample, feature, and period levels. It discusses the benefits, drawbacks, challenges, and opportunities of various data-centric approaches for time-series data processing. The authors highlight the importance of prioritizing data quality in today’s large language models and emphasize the need to shift focus from model refinement to data-centric AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data scientists are working on a new approach to time series analysis using machine learning techniques. This requires good quality data, which is important for making accurate predictions and saving resources. The paper looks at different ways of processing time-series data and proposes a system to group these methods together based on the type of data they work with. It also talks about the advantages and disadvantages of each method and suggests areas where more research is needed. |
Keywords
» Artificial intelligence » Machine learning » Time series
