The Collective of Transform Ensembles (COTE) for Time Series Classification

用于时间序列分类的变换集成集合体 (COTE)

• 批准号: EP/M015807/1
• 负责人: Anthony Bagnall
• 金额: $ 40.49万
• 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: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM015807%2F1
• 关键词: Collective; Transform; Ensembles; COTE; Time

Time series classification is the problem of trying to predict an outcome based on a series of ordered data. So, for example, if we take a series of electronic readings from a sample of meat, the classification problem could be to determine whether that sample is pure beef or whether it has been adulterated with some other meat. Alternatively, if we have a series of electricity usage, the classification problem could be to determine which type of device generated those readings. Time series classification problems arise in all areas of science, and we have worked on problems involving ECG and EEG data, chemical concentration readings, astronomical measurements, otolith outlines, electricity usage, food spectrographs, hand and bone radiograph data and mutant worm motion. The algorithm we have developed to do this, The Collective of Transform Ensembles (COTE), is significantly better than any other technique proposed in the literature (when assessed on 80 data sets used in the literature). This project looks to improve COTE further and to apply it to three problem domains of genuine importance to society. In collaboration with Imperial, we will look at classifying Caenorhabditis elegans via motion traces. C. elegans is a nematode worm commonly used as a model organism in the study of genetics. We will help develop an automated classifier for C. elegans mutant types based on their motion, with the objective of identifying genes that regulate appetite. This classifier will automate a task previously done manually at great cost and will uncover conserved regulators of appetite in a model organism in which functional dissection is possible at the level of behaviour, neural circuitry, and fat storage. In the long term, this may give insights into the genetic component of human obesity.Working closely with the Institute of Food Research (IFR), we will attempt to solve two problems involving classifying food types by their molecular spectra (infrared, IR, and nuclear magnetic resonance, NMR). The first problem involves classifying meat type. The horse meat scandal of 2012/3 has shown that there is an urgent need to increase current authenticity testing regimes for meat. IFR have been working closely with a company called Oxford Instruments to develop a new low-cost, bench-top spectrometer called the Pulsar for rapid screening of meat. We will collaborate with IFR to find the best algorithms for performing this classification. The second problem aims to find non-destructive ways for testing whether the content of intact spirits bottles is genuine or fake. Forged alcohol is commonplace, and in recent years there has been an increasing number of serious injuries and even deaths from the consumption of illegally produced spirits. The development of sensor technology to detect this type of fraud would thus have great societal value, and the collaboration with Oxford Instruments offers the potential for the development of portable scanners for product verification.Our third case study involves classifying electric devices from smart meter data. Currently 25% of the United Kingdom's greenhouse gasses are accounted for by domestic energy consumption, such as heating, lighting and appliance use. The government has committed to an 80% reduction of CO2 emissions by 2050, and to meet this is requiring the installation of smart energy meters in every household to promote energy saving. The primary output of this investment of billions of pounds in technology will be enormous quantities of data relating to electricity usage. Understanding and intelligently using this data will be crucial if we are to meet the emissions target. We will focus on one part of the analysis, which is the problem of determining whether we can automatically classify the nature of the device(s) currently consuming electricity at any point in time. This is a necessary first step in better understanding household practices, which is essential for reducing usage.

时间序列分类是试图根据一系列有序数据预测结果的问题。因此，例如，如果我们从肉类样品中获取一系列电子读数，分类问题可能是确定该样品是纯牛肉还是掺杂了其他肉类。或者，如果我们有一系列的用电量，分类问题可能是确定哪种类型的设备生成这些读数。时间序列分类问题出现在所有科学领域，我们已经研究了涉及ECG和EEG数据，化学浓度读数，天文测量，耳石轮廓，电力使用，食物光谱，手和骨射线数据和突变蠕虫运动的问题。我们已经开发了这样做的算法，集体的转换集成（COTE），是显着优于文献中提出的任何其他技术（当评估文献中使用的80个数据集）。该项目旨在进一步改进COTE，并将其应用于对社会真正重要的三个问题领域。与帝国合作，我们将研究通过运动轨迹对秀丽隐杆线虫进行分类。C.秀丽线虫是一种线虫，通常用作遗传学研究的模式生物。我们将帮助开发一个C语言的自动分类器。根据它们的运动，研究了线虫的突变类型，目的是确定调节食欲的基因。这种分类器将自动化以前手动完成的任务，成本很高，并将发现保守的调节食欲的模式生物，其中功能解剖是可能的行为，神经回路和脂肪储存的水平。从长远来看，这可能会让我们深入了解人类肥胖的遗传成分。我们将与食品研究所（IFR）密切合作，试图解决两个问题，包括根据分子光谱（红外线，IR和核磁共振，NMR）对食物类型进行分类。第一个问题涉及肉类的分类。2012/3年的马肉丑闻表明，迫切需要加强目前的肉类真实性检测制度。IFR一直在与一家名为牛津仪器的公司密切合作，开发一种新的低成本台式光谱仪，称为脉冲星，用于快速筛选肉类。我们将与IFR合作，寻找执行此分类的最佳算法。第二个问题是如何以非破坏性的方法，检验完整的酒樽内的酒是真是假。假酒司空见惯，近年来，因食用非法生产的烈酒而造成严重伤害甚至死亡的事件越来越多。因此，开发检测此类欺诈的传感器技术将具有巨大的社会价值，与Oxford Instruments的合作为开发用于产品验证的便携式扫描仪提供了潜力。我们的第三个案例研究涉及从智能电表数据中对电子设备进行分类。目前，英国25%的温室气体来自家庭能源消耗，如取暖、照明和电器使用。政府承诺到2050年将二氧化碳排放量减少80%，为了实现这一目标，需要在每个家庭安装智能电表，以促进节能。这项数十亿英镑的技术投资的主要产出将是与用电量有关的大量数据。如果我们要实现排放目标，理解并明智地使用这些数据将至关重要。我们将集中在分析的一个部分，即确定我们是否可以在任何时间点自动分类当前消耗电力的设备的性质的问题。这是更好地了解家庭做法的必要的第一步，这对减少使用至关重要。

项目成果

Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles

• DOI: 10.1109/tkde.2015.2416723
• 发表时间: 2015-09-01
• 期刊: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
• 影响因子: 8.9
• 作者: Bagnall, Anthony;Lines, Jason;Bostrom, Aaron
• 通讯作者: Bostrom, Aaron

Detecting Electric Devices in 3D Images of Bags

检测包袋 3D 图像中的电子设备

• DOI: 10.48550/arxiv.2005.02163
• 发表时间: 2020
• 作者: Bagnall A

Optimizing dynamic time warping's window width for time series data mining applications

• DOI: 10.1007/s10618-018-0565-y
• 发表时间: 2018-07-01
• 期刊: DATA MINING AND KNOWLEDGE DISCOVERY
• 影响因子: 4.8
• 作者: Hoang Anh Dau;Silva, Diego Furtado;Keogh, Eamonn
• 通讯作者: Keogh, Eamonn

Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series

• DOI: 10.1109/bigdata.2017.8258009
• 发表时间: 2017-12
• 期刊: 2017 IEEE International Conference on Big Data (Big Data)
• 作者: Hoang Anh Dau;Diego Furtado Silva;F. Petitjean;G. Forestier;A. Bagnall;Eamonn J. Keogh