Statistical comparisons of time-series feature sets on classification tasks
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时间序列特征集在分类任务中的统计比较
- 研究问题
- 不同规模和构成的单变量时间序列特征集，在分类问题上有何相对优势，整体表现是否掩盖了具体任务中的明显差异？
- 方法与思路
- 作者在一百二十四个分类问题中比较六套开源特征集与三套基线，使用基于归一化的问题层面基准替代仅按排名比较。
- 材料陈述的贡献
- 摘要称多数成对比较打平，最大特征集总体胜率最高，同时展示特定特征构成的优势与劣势，以及简单频谱和分位数基线的表现。
- 阅读关注点
- 应核对归一化指标、打平与胜出的统计判定、分类器和验证配置，以及计算成本与任务差异如何影响比较结论。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未经核验；摘要未说明完整比较协议，所报总体胜率不能推出任意新任务上的性能优势。
原文摘要
In recent years, numerous open-source software libraries have been developed for computing sets of features from univariate time series. The type and number of features vary across these feature sets, which have been constructed with varying disciplinary perspectives on quantifying structure in time-series data. To date, the relative strengths and weaknesses of these feature sets on time-series classification problems remains largely unexplored. Here we aimed to understand the relative performance of six open-source feature sets and three baseline feature sets (based on distributional and/or basic spectral structure) across 124 univariate time-series classification problems using a normalization-based approach to problem-level benchmarking that better indexes the relative strengths and weaknesses of different algorithms compared to prior rank-based approaches. Despite their dramatic differences in size, composition, and computation time, we found that feature sets performed relatively similarly overall (85.3% of pairwise comparisons resulted in ties), with the largest feature set, tsfresh, exhibiting the strongest overall performance (29.03% wins across all pairwise comparisons against other feature sets). We also highlighted specific problems on which the specific composition of a given feature set gave it a substantial performance advantage or disadvantage, and problems where simple baselines comprised of Fourier coefficients and quantiles were sufficient to achieve strong performance. Our results demonstrate the need to consider problem-level performance when benchmarking time-series feature sets, and highlight the importance of feature make-up in driving relative classification performance.