aeon: a toolkit for machine learning with time series

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。它们已成为从业人员的主要工具和科学、商业和工业应用的核心组成部分。但是，尽管时间序列数据无处不在，直到最近，还没有这样的框架用于时间序列的机器学习。2019年，sktime被认为填补了这一空白，它已经成为学术界和工业界广泛使用的时间序列分析工具包和软件组件。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旨在通过为相关的时间序列任务，如预测、分类、聚类、回归、注释、异常检测和分段提供统一的接口，将这些社区联系起来。它提供了scikit-learn兼容的算法，并可以轻松访问其他软件包中无法访问的最先进算法的实现。该项目将通过提供专用维护资源，增强功能并增加与科学和工业利益相关者的参与，使sktime能够继续维持和发展其运营。我们希望扩展sktime的功能，以包括活跃的机器学习研究的新领域，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和更快地应用于真正的科学兴趣的真实的世界问题，从而将理论与实践联系起来。为了展示这种潜力，我们将与领域专家合作开发两个应用程序。第一个涉及使用脑电图（EEG）预测痴呆症的早期发作。脑电图是一种时间序列，它使用放置在头皮上的一系列电极记录大脑中的电活动。该设备相对便宜和便携。如果我们可以用它来筛查早发性痴呆症，它可能会对许多患者的结果产生巨大的影响。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胜过传统方法。第二个应用程序涉及分析从大奥蒙德街医院（GOSH）的儿童重症监护监测产生的数据。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）。越来越多的时间序列数据被捕获，并可以被挖掘以改善临床实践。我们将与已经与GOSH合作的研究团队合作，探索sktime是否可以用于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“何时是拔出帮助患者呼吸的管子的最佳时间？".它还将帮助我们实现加快发现和传播最佳做法的更广泛目标。医院之间的数据共享非常困难和耗时。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，例如，如果我们在GOSH数据中发现了一些有趣的东西，我们希望快速分享这个发现以及在我们的数据中验证它的代码。通过sktime共享代码将大大减少在不同观察数据集上测试假设所需的时间，并使不同研究人员对独立患者组进行透明验证的结果更具信心。

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.