FRUITS：利用迭代和进行时间序列分类的特征提取

IF 2.8 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Data Mining and Knowledge Discovery Pub Date : 2024-09-14 DOI:10.1007/s10618-024-01068-1

Joscha Diehl, Richard Krieg

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引用次数: 0

摘要

我们介绍了一种时间序列分类方法，它根据迭和特征（ISS）提取特征，然后应用线性分类器。这些特征本质上是非线性的，能捕捉时间信息，而且在某些设置下，不受某种形式的时间扭曲的影响。我们在 UCR 档案中取得了极具竞争力的准确性和速度。我们在 https://github.com/irkri/fruits 网站上提供了我们的代码。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

FRUITS: feature extraction using iterated sums for time series classification

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FRUITS: feature extraction using iterated sums for time series classification

We introduce a pipeline for time series classification that extracts features based on the iterated-sums signature (ISS) and then applies a linear classifier. These features are intrinsically nonlinear, capture chronological information, and, under certain settings, are invariant to a form of time-warping. We achieve competitive results, both in accuracy and speed, on the UCR archive. We make our code available at https://github.com/irkri/fruits.

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来源期刊

Data Mining and Knowledge Discovery 工程技术-计算机：人工智能

CiteScore

10.40

自引率

4.20%

发文量

审稿时长

10 months

期刊介绍： Advances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis. Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing.