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

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。例如，如果我们从一份肉类样本中获取一系列电子读数，分类问题可能是确定该样本是纯牛肉还是掺入了其他一些肉类。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产生了这些读数。时间序列分类问题出现在科学的所有领域，我们致力于解决涉及心电和脑电数据、化学浓度读数、天文测量、耳石轮廓、用电量、食物光谱分析、手部和骨骼X光数据以及变异蠕虫运动的问题。我们为此开发的算法，变换集合集成(COTE)，明显优于文献中提出的任何其他技术(当在文献中使用的80个数据集上进行评估时)。该项目旨在进一步改进COTE，并将其应用于对社会具有真正重要性的三个问题领域。在与Imperial的合作中，我们将研究通过运动轨迹对秀丽线虫进行分类。线虫是一种线虫，通常被用作遗传学研究的模式生物。我们将帮助开发一个基于线虫运动的突变类型的自动分类器，目的是识别调节食欲的基因。这种分类器将以高昂的成本自动完成以前手动完成的任务，并将在模型生物体中发现保守的食欲调节因子，在该模型生物体中，在行为、神经回路和脂肪储存水平上可能进行功能解剖。从长远来看，这可能会对人类肥胖的遗传成分有更深入的了解。我们将与食品研究所(IFR)密切合作，试图解决两个涉及根据分子光谱(红外、红外和核磁共振)对食物类型进行分类的问题。第一个问题涉及肉类类型的分类。2012/3年度的马肉丑闻表明，迫切需要加强目前的马肉真实性测试制度。IFR一直在与一家名为牛津仪器的公司密切合作，开发一种名为Pulsar的新型低成本台式光谱仪，用于快速筛选肉类。我们将与IFR合作，找到执行这种分类的最佳算法。第二个问题旨在找到无损的方法来检测完好无损的烈酒瓶子里的东西是真是假。伪造酒精很常见，近年来，因饮用非法生产的烈酒而严重受伤甚至死亡的事件越来越多。因此，传感器技术的发展将具有巨大的社会价值，与牛津仪器公司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%的温室气体是由家庭能源消耗造成的，如供暖、照明和家电使用。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能表，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电力使用有关的大量数据。如果我们要实现排放目标，理解和明智地使用这些数据将是至关重要的。我们将集中在分析的一个部分，即确定我们是否可以自动对当前在任何时间点消耗电力的设备(S)的性质进行分类的问题。这是更好地了解家庭做法的必要的第一步，这对减少使用量至关重要。

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.