2 research outputs found
Generalised Interpretable Shapelets for Irregular Time Series
The shapelet transform is a form of feature extraction for time series, in
which a time series is described by its similarity to each of a collection of
`shapelets'. However it has previously suffered from a number of limitations,
such as being limited to regularly-spaced fully-observed time series, and
having to choose between efficient training and interpretability. Here, we
extend the method to continuous time, and in doing so handle the general case
of irregularly-sampled partially-observed multivariate time series.
Furthermore, we show that a simple regularisation penalty may be used to train
efficiently without sacrificing interpretability. The continuous-time
formulation additionally allows for learning the length of each shapelet
(previously a discrete object) in a differentiable manner. Finally, we
demonstrate that the measure of similarity between time series may be
generalised to a learnt pseudometric. We validate our method by demonstrating
its performance and interpretability on several datasets; for example we
discover (purely from data) that the digits 5 and 6 may be distinguished by the
chirality of their bottom loop, and that a kind of spectral gap exists in
spoken audio classification
GENDIS : genetic discovery of shapelets
In the time series classification domain, shapelets are subsequences that are discriminative of a certain class. It has been shown that classifiers are able to achieve state-of-the-art results by taking the distances from the input time series to different discriminative shapelets as the input. Additionally, these shapelets can be visualized and thus possess an interpretable characteristic, making them appealing in critical domains, where longitudinal data are ubiquitous. In this study, a new paradigm for shapelet discovery is proposed, which is based on evolutionary computation. The advantages of the proposed approach are that: (i) it is gradient-free, which could allow escaping from local optima more easily and supports non-differentiable objectives; (ii) no brute-force search is required, making the algorithm scalable; (iii) the total amount of shapelets and the length of each of these shapelets are evolved jointly with the shapelets themselves, alleviating the need to specify this beforehand; (iv) entire sets are evaluated at once as opposed to single shapelets, which results in smaller final sets with fewer similar shapelets that result in similar predictive performances; and (v) the discovered shapelets do not need to be a subsequence of the input time series. We present the results of the experiments, which validate the enumerated advantages