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

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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.