aeon: a toolkit for machine learning with time series

aeon：时间序列机器学习工具包

• 批准号: EP/W030756/1
• 负责人: Anthony Bagnall
• 金额: $ 68.13万
• 依托单位: University of East Anglia
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW030756%2F1
• 关键词: aeon; toolkit; machine; learning; time

In recent years, machine learning frameworks such as scikit-learn have become essential infrastructure of modern data science. They have become the principal tool for practitioners and central components in scientific, commercial and industrial applications. But despite the ubiquity of time series data, until recently, no such framework exists for machine learning with time series. In 2019, sktime was conceived to fill this gap and it has become an established toolkit and software component for time series analysis used world-wide by academics and industry alike.It is an easy-to-use, flexible and modular framework for a wide range of time series machine learning tasks. Techniques for learning from time series have been developed in a range of disciplines, including: statistics; machine learning; signal processing; econometrics; and finance. sktime aims to link these communities by providing a unified interface for related time series tasks such as forecasting, classification, clustering, regression, annotation, anomaly detection and segmentation. It provides scikit-learn compatible algorithms and gives easy access to implementations of state of the art algorithms not accessible in other packages. This project will allow sktime to continue to sustain and grow its operations by providing dedicated maintenance resource, enhancing the functionality and increasing engagement with scientific and industrial stakeholders. We wish to broaden the functionality of sktime to include new areas of active machine learning research and deepen our user base to reach new communities of researchers. Our aim is to link theory and practice by making it easier and faster for state of the art time series algorithms to be applied to real world problems of genuine scientific interest. To demonstrate this potential we will collaborate with domain experts on two applications. The first relates to predicting the early onset of dementia using electroencephalography (EEG). EEG are time series that record electrical activity in the brain using a series electrodes placed on the scalp. The equipment is relatively cheap and portable. If we could use it to screen for early onset dementia it could make a huge difference to the outcomes for many patients. However, the accuracy needed for clinical use is very hard to achieve. We will collaborate with experts in Cambridge who have clinical data and see if the state of the art predictive models can outperform traditional approaches. The second application involves analysing data generated from intensive care monitoring of children in Great Ormond Street Hospital (GOSH). Intensive care patients are continually monitored for vital body functions (heart rate, blood pressure, breathing rate, etc). Increasingly, this time series data is captured and can be mined to improve clinical practice. We will collaborate with a research team already working with GOSH to explore whether sktime can be used to decrease the time it takes to analyse this data.This research may lead to insights that improve clinical practice by answering questions such as "when is the best time to remove the tube that is helping a patient breathe?". It will also help us reach our broader goal to speed up the discovery and dissemination of best practice. Data sharing between hospitals is, quite sensibly, difficult and time consuming. We wish to develop a new user base of hospital data scientists willing to share their research findings and code rather than their data. So, for example, if we discover something interesting in the GOSH data, we would like to rapidly share this finding and the code that verifies it in our data. This code sharing via sktime will dramatically reduce the time taken to test hypotheses on different observational data sets and give greater confidence in finding verified on independent groups of patients conducted transparently by different researchers.

近年来，scikit-learn等机器学习框架已成为现代数据科学的重要基础设施。它们已成为科学、商业和工业应用领域从业者和核心组成部分的主要工具。但是，尽管时间序列数据无处不在，但直到最近，还没有这样的用于时间序列机器学习的框架。 2019 年，sktime 的诞生旨在填补这一空白，它已成为学术界和业界广泛使用的时间序列分析的既定工具包和软件组件。它是一个易于使用、灵活和模块化的框架，适用于各种时间序列机器学习任务。从时间序列中学习的技术已经在一系列学科中得到发展，包括：统计学；机器学习；信号处理；计量经济学；和金融。 sktime 旨在通过为相关时间序列任务（如预测、分类、聚类、回归、注释、异常检测和分割）提供统一的接口来链接这些社区。它提供了 scikit-learn 兼容算法，并可以轻松访问其他软件包无法访问的最先进算法的实现。该项目将使 sktime 通过提供专用维护资源、增强功能以​​及增加与科学和工业利益相关者的接触来继续维持和发展其运营。我们希望扩大 sktime 的功能，将主动机器学习研究的新领域纳入其中，并深化我们的用户群，以接触新的研究人员社区。我们的目标是通过使最先进的时间序列算法更容易、更快速地应用于真正具有科学意义的现实世界问题，将理论与实践联系起来。为了展示这种潜力，我们将与领域专家就两个应用程序进行合作。第一个涉及使用脑电图（EEG）预测痴呆症的早期发作。脑电图是使用放置在头皮上的一系列电极记录大脑电活动的时间序列。该设备相对便宜且便携。如果我们可以用它来筛查早发性痴呆症，可能会对许多患者的治疗结果产生巨大影响。然而，临床使用所需的准确性很难达到。我们将与拥有临床数据的剑桥专家合作，看看最先进的预测模型是否能够超越传统方法。第二个应用程序涉及分析大奥蒙德街医院 (GOSH) 儿童重症监护监测生成的数据。重症监护患者持续监测重要的身体功能（心率、血压、呼吸频率等）。人们越来越多地捕获并挖掘这些时间序列数据，以改善临床实践。我们将与一个已经与 GOSH 合作的研究团队合作，探索是否可以使用 sktime 来减少分析这些数据所需的时间。这项研究可能会通过回答诸如“何时是拔除帮助患者呼吸的管子的最佳时间？”等问题来产生改善临床实践的见解。它还将帮助我们实现更广泛的目标，即加快最佳实践的发现和传播。医院之间的数据共享显然是困难且耗时的。我们希望开发一个新的医院数据科学家用户群，他们愿意分享他们的研究成果和代码，而不是他们的数据。因此，举例来说，如果我们在 GOSH 数据中发现一些有趣的东西，我们希望快速分享这一发现以及在我们的数据中验证它的代码。通过 sktime 进行的代码共享将大大减少在不同观察数据集上测试假设所需的时间，并为在不同研究人员透明地进行的独立患者组上进行验证提供更大的信心。

项目成果

A Review and Evaluation of Elastic Distance Functions for Time Series Clustering

• DOI: 10.1007/s10115-023-01952-0
• 发表时间: 2022-05
• 期刊: Knowl. Inf. Syst.
• 作者: Christopher Holder;Matthew Middlehurst;A. Bagnall

Bake off redux: a review and experimental evaluation of recent time series classification algorithms

Bake off redux：近期时间序列分类算法的回顾和实验评估

• DOI: 10.48550/arxiv.2304.13029
• 发表时间: 2023
• 作者: Middlehurst M

Advanced Analytics and Learning on Temporal Data - 8th ECML PKDD Workshop, AALTD 2023, Turin, Italy, September 18-22, 2023, Revised Selected Papers

时态数据的高级分析和学习 - 第 8 届 ECML PKDD 研讨会，AALTD 2023，意大利都灵，2023 年 9 月 18-22 日，修订后的精选论文

• DOI: 10.1007/978-3-031-49896-1_4
• 发表时间: 2023
• 作者: Holder C

Advances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19-21, 2023, Proceedings, Part I

计算智能的进展 - 第 17 届国际人工神经网络工作会议，IWANN 2023，葡萄牙蓬塔德尔加达，2023 年 6 月 19-21 日，会议记录，第一部分

• DOI: 10.1007/978-3-031-43085-5_48
• 发表时间: 2023
• 作者: Rushbrooke A

Convolutional and Deep Learning based techniques for Time Series Ordinal Classification

• DOI: 10.48550/arxiv.2306.10084
• 发表时间: 2023-06
• 期刊: ArXiv
• 作者: Rafael Ayll'on-Gavil'an;David Guijo-Rubio;Pedro-Antonio Guti'errez;A. Bagnall;C. Herv'as-Mart'inez