Feature-Based Time-Series Analysis in R using the theft Package
使用 R 包 theft 开展基于特征的时间序列分析AI 解读 ↓
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使用 R 包 theft 开展基于特征的时间序列分析
- 研究问题
- 摘要研究如何统一访问多语言的时间序列特征工具，并提供从特征提取到解释、可视化和分类的标准化分析流程。
- 方法与思路
- 作者提出可扩展的 R 包 theft，整合六套开源特征集合，并提供特征处理、低维投影、可视化模板和分类功能。
- 材料陈述的贡献
- 摘要称该包解决特征工具入口分散和跨语言使用的问题，形成用于量化与解释时间序列结构的统一软件框架。
- 阅读关注点
- 应核对跨语言依赖、特征预处理和缺失值处理，并检查分类流程的数据划分是否避免特征筛选或标准化带来的泄漏。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未提供运行性能、分类提升数值或当前软件兼容性。
原文摘要
Time series are measured and analyzed across the sciences. One method of quantifying the structure of time series is by calculating a set of summary statistics or `features', and then representing a time series in terms of its properties as a feature vector. The resulting feature space is interpretable and informative, and enables conventional statistical learning approaches, including clustering, regression, and classification, to be applied to time-series datasets. Many open-source software packages for computing sets of time-series features exist across multiple programming languages, including catch22 (22 features: Matlab, R, Python, Julia), feasts (42 features: R), tsfeatures (63 features: R), Kats (40 features: Python), tsfresh (779 features: Python), and TSFEL (390 features: Python). However, there are several issues: (i) a singular access point to these packages is not currently available; (ii) to access all feature sets, users must be fluent in multiple languages; and (iii) these feature-extraction packages lack extensive accompanying methodological pipelines for performing feature-based time-series analysis, such as applications to time-series classification. Here we introduce a solution to these issues in an R software package called theft: Tools for Handling Extraction of Features from Time series. theft is a unified and extendable framework for computing features from the six open-source time-series feature sets listed above. It also includes a suite of functions for processing and interpreting the performance of extracted features, including extensive data-visualization templates, low-dimensional projections, and time-series classification operations. With an increasing volume and complexity of time-series datasets in the sciences and industry, theft provides a standardized framework for comprehensively quantifying and interpreting informative structure in time series.