Anthony J. Bagnall

The next release problem

Companies developing and maintaining complex software systems need to determine the features that should be added to their system as part of the next release. They will wish to select these features to ensure the demands of their client base are satisfied as much as possible while at the same time ensuring that they themselves have the resources to undertake the necessary development. This situation is modelled in this paper and the problem of selecting an optimal next release is shown to be NP-hard. The use of various modern heuristics to find a high quality but possibly suboptimal solution is described. Comparative studies of these heuristics are given for various test cases.

Classification of time series by shapelet transformation

Time-series classification (TSC) problems present a specific challenge for classification algorithms: how to measure similarity between series. A shapelet is a time-series subsequence that allows for TSC based on local, phase-independent similarity in shape. Shapelet-based classification uses the similarity between a shapelet and a series as a discriminatory feature. One benefit of the shapelet approach is that shapelets are comprehensible, and can offer insight into the problem domain. The original shapelet-based classifier embeds the shapelet-discovery algorithm in a decision tree, and uses information gain to assess the quality of candidates, finding a new shapelet at each node of the tree through an enumerative search. Subsequent research has focused mainly on techniques to speed up the search. We examine how best to use the shapelet primitive to construct classifiers. We propose a single-scan shapelet algorithm that finds the best

A novel bit level time series representation with implication of similarity search and clustering

k

Clustering time series from ARMA models with clipped data

k shapelets, which are used to produce a transformed dataset, where each of the

Time series classification with ensembles of elastic distance measures

k

Clustering Time Series with Clipped Data

k features represent the distance between a time series and a shapelet. The primary advantages over the embedded approach are that the transformed data can be used in conjunction with any classifier, and that there is no recursive search for shapelets. We demonstrate that the transformed data, in conjunction with more complex classifiers, gives greater accuracy than the embedded shapelet tree. We also evaluate three similarity measures that produce equivalent results to information gain in less time. Finally, we show that by conducting post-transform clustering of shapelets, we can enhance the interpretability of the transformed data. We conduct our experiments on 29 datasets: 17 from the UCR repository, and 12 we provide ourselves.