Time series classification with ensembles of elastic distance measures

Several alternative distance measures for comparing time series have recently been proposed and evaluated on time series classification (TSC) problems. These include variants of dynamic time warping (DTW), such as weighted and derivative DTW, and edit distance-based measures, including longest common subsequence, edit distance with real penalty, time warp with edit, and move–split–merge. These measures have the common characteristic that they operate in the time domain and compensate for potential localised misalignment through some elastic adjustment. Our aim is to experimentally test two hypotheses related to these distance measures. Firstly, we test whether there is any significant difference in accuracy for TSC problems between nearest neighbour classifiers using these distance measures. Secondly, we test whether combining these elastic distance measures through simple ensemble schemes gives significantly better accuracy. We test these hypotheses by carrying out one of the largest experimental studies ever conducted into time series classification. Our first key finding is that there is no significant difference between the elastic distance measures in terms of classification accuracy on our data sets. Our second finding, and the major contribution of this work, is to define an ensemble classifier that significantly outperforms the individual classifiers. We also demonstrate that the ensemble is more accurate than approaches not based in the time domain. Nearly all TSC papers in the data mining literature cite DTW (with warping window set through cross validation) as the benchmark for comparison. We believe that our ensemble is the first ever classifier to significantly outperform DTW and as such raises the bar for future work in this area

A Significantly Faster Elastic-Ensemble for Time-Series Classification

The Elastic-Ensemble [7] has one of the longest build times of all constituents of the current state of the art algorithm for time series classification: the Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) [8]. We investigate two simple and intuitive techniques to reduce the time spent training the Elastic Ensemble to consequently reduce HIVE-COTE train time. Our techniques reduce the effort involved in tuning parameters of each constituent nearest-neighbour classifier of the Elastic Ensemble. Firstly, we decrease the parameter space of each constituent to reduce tuning effort. Secondly, we limit the number of training series in each nearest neighbour classifier to reduce parameter option evaluation times during tuning. Experimentation over 10-folds of the UEA/UCR time-series classification problems show both techniques and give much faster build times and, crucially, the combination of both techniques give even greater speedup, all without significant loss in accuracy

On the segmentation and classification of hand radiographs

This research is part of a wider project to build predictive models of bone age using hand radiograph images. We examine ways of finding the outline of a hand from an X-ray as the first stage in segmenting the image into constituent bones. We assess a variety of algorithms including contouring, which has not previously been used in this context. We introduce a novel ensemble algorithm for combining outlines using two voting schemes, a likelihood ratio test and dynamic time warping (DTW). Our goal is to minimize the human intervention required, hence we investigate alternative ways of training a classifier to determine whether an outline is in fact correct or not. We evaluate outlining and classification on a set of 1370 images. We conclude that ensembling with DTW improves performance of all outlining algorithms, that the contouring algorithm used with the DTW ensemble performs the best of those assessed, and that the most effective classifier of hand outlines assessed is a random forest applied to outlines transformed into principal components

The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

In the last five years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series classification archive. The archive has recently been expanded to 85 data sets, over half of which have been donated by researchers at the University of East Anglia. Aspects of previous evaluations have made comparisons between algorithms difficult. For example, several different programming languages have been used, experiments involved a single train/test split and some used normalised data whilst others did not. The relaunch of the archive provides a timely opportunity to thoroughly evaluate algorithms on a larger number of datasets. We have implemented 18 recently proposed algorithms in a common Java framework and compared them against two standard benchmark classifiers (and each other) by performing 100 resampling experiments on each of the 85 datasets. We use these results to test several hypotheses relating to whether the algorithms are significantly more accurate than the benchmarks and each other. Our results indicate that only 9 of these algorithms are significantly more accurate than both benchmarks and that one classifier, the Collective of Transformation Ensembles, is significantly more accurate than all of the others. All of our experiments and results are reproducible: we release all of our code, results and experimental details and we hope these experiments form the basis for more rigorous testing of new algorithms in the future

Large isotropic negative thermal expansion above a structural quantum phase transition

Perovskite structured materials contain myriad tunable ordered phases of electronic and magnetic origin with proven technological importance and strong promise for a variety of energy solutions. An always-contributing influence beneath these cooperative and competing interactions is the lattice, whose physics may be obscured in complex perovskites by the many coupled degrees of freedom, which makes these systems interesting. Here, we report signatures of an approach to a quantum phase transition very near the ground state of the nonmagnetic, ionic insulating, simple cubic perovskite material ScF3, and show that its physical properties are strongly effected as much as 100 K above the putative transition. Spatial and temporal correlations in the high-symmetry cubic phase determined using energy- and momentum-resolved inelastic x-ray scattering as well as x-ray diffraction reveal that soft mode, central peak, and thermal expansion phenomena are all strongly influenced by the transition.National Science Foundation Award No. DMR-1506825US Department of Energy, Office of Basic Energy Sciences under Contract No. DE- AC02-06CH11357Yale Prize Postdoctoral FellowshipNSF Grant No. DMR-0115852Universidad de Costa Rica. Vicerrectoría de Investigación Projecto No. 816-B5-220UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Ciencia e Ingeniería de Materiales (CICIMA

A Modified Experimental Hut Design for Studying Responses of Disease-Transmitting Mosquitoes to Indoor Interventions: The Ifakara Experimental Huts

Differences between individual human houses can confound results of studies aimed at evaluating indoor vector control interventions such as insecticide treated nets (ITNs) and indoor residual insecticide spraying (IRS). Specially designed and standardised experimental huts have historically provided a solution to this challenge, with an added advantage that they can be fitted with special interception traps to sample entering or exiting mosquitoes. However, many of these experimental hut designs have a number of limitations, for example: 1) inability to sample mosquitoes on all sides of huts, 2) increased likelihood of live mosquitoes flying out of the huts, leaving mainly dead ones, 3) difficulties of cleaning the huts when a new insecticide is to be tested, and 4) the generally small size of the experimental huts, which can misrepresent actual local house sizes or airflow dynamics in the local houses. Here, we describe a modified experimental hut design - The Ifakara Experimental Huts- and explain how these huts can be used to more realistically monitor behavioural and physiological responses of wild, free-flying disease-transmitting mosquitoes, including the African malaria vectors of the species complexes Anopheles gambiae and Anopheles funestus, to indoor vector control-technologies including ITNs and IRS. Important characteristics of the Ifakara experimental huts include: 1) interception traps fitted onto eave spaces and windows, 2) use of eave baffles (panels that direct mosquito movement) to control exit of live mosquitoes through the eave spaces, 3) use of replaceable wall panels and ceilings, which allow safe insecticide disposal and reuse of the huts to test different insecticides in successive periods, 4) the kit format of the huts allowing portability and 5) an improved suite of entomological procedures to maximise data quality