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This work presents four novel entropy-based indices for measuring the community evolution of developer social networks (DSNs) in open source software (OSS) projects. The proposed indices offer a quantitative measure of community split, shrink, merge, and ...

• Introduces Constrained DTW Preserving shapelets (CDPS).
• A means for semi-supervised learning of shapelets using must-link and cannot-link constraints.
• Introduces Shapelet Cluster Explanation, including several variants, for ...

The analysis of time series is becoming ever more popular due to the proliferation of sensors. A well-known similarity measure for time-series is Dynamic Time Warping (DTW), which does not respect the axioms of a metric. These, however, can be ...

The recognition of operational state patterns is crucial for the safe, reliable and profitable operational management of integrated energy systems (IES). However, considering that the monitored operational data are often not labelled, supervised ...

Compared to the full-length methods for time series classification, shapelet-based methods acquire better interpretation, higher efficiency and precision since shapelets are discriminative features that well represent a time series. However, ...

The analysis of time series for clustering and classification is becoming ever more popular because of the increasingly ubiquitous nature of IoT, satellite constellations, and handheld and smart-wearable devices, etc. The presence of phase shift, ...

Anomaly detection in time is an important task in many applications. Sensors are deployed in the industrial site to monitor the condition of different attributes or different places in real time, which generate multivariate time series. Recently, ...

Shapelet-based algorithms are widely used for time series classification because of their ease of interpretation, but they are currently outperformed by recent state-of-the-art approaches. We present a new formulation of time series shapelets ...

Human-human interactions recognition has high potential to have a big impact on enabling robots being able to interact with people. Recently, body sensor networks (BSNs) have been widely applied in many fields. In this paper, we investigate the ...

• Classifying time series with Euclidean distance is robust against adversarial attacks.

Deep neural networks have been shown to be vulnerable against specifically-crafted perturbations designed to affect their predictive performance. Such perturbations, formally termed ‘adversarial attacks’ have been designed for various ...

Shapelets are discriminative subsequences extracted from time-series data. Classifiers using shapelets have proven to achieve performances competitive to state-of-the-art methods, while enhancing the model’s interpretability. While a lot of ...

Time series classification has a wide variety of possible applications, like outlines of figure types, signs of movement and sensor signals. This diversity may present different results with the application of machine learning techniques. Among ...

Early classification on time series has attracted much attention in time-sensitive domains. The goal of early classification on time series is to achieve better classification accuracy, and meanwhile to make prediction as early as possible. ...

Time series classification has been considered as one of the most challenging problems in data mining and is widely used in a broad range of fields. A biased distribution leads to classification on minority time series objects more severe. A ...

Shapelets are phase independent subseries that can be used to discriminate between time series. Shapelets have proved to be very effective primitives for time series classification. The two most prominent shapelet based classification algorithms ...

Shapelet models have attracted a lot of attention from researchers in the time series community, due in particular to its good classification performance. However, such models only inform about the presence/absence of local temporal patterns. ...

Real-world time series classification applications often involve positive unlabeled (PU) training data, where there are only a small set PL of positive labeled examples and a large set U of unlabeled ones. Most existing time series PU ...

Transformation of multivariate time series into feature spaces are common for data mining tasks like classification. Ordinality is one important property in time series that provides a qualitative representation of the underlying dynamic regime. ...

Time series classification (TSC) has been an ongoing machine learning problem with countless proposed algorithms spanning a multitude of fields. Whole series, intervals, shapelet, dictionary-based, and model-based are all different past approaches ...

A primary challenge of time series classification is how to extract powerful features from training samples. Two kinds of classification methods, global-based and local-based methods, have been studied widely in recent years. The global-based methods, ...

In the last 5 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 ...