Time series discords are a useful primitive for time series anomaly detection, and the matrix
profile is capable of capturing discord effectively. There exist many research efforts to …

Edge devices that operate in real-world environments are subjected to unpredictable
conditions caused by environmental forces such as wind and uneven surfaces. Since most …

Recurrent Neural Networks (RNNs) have achieved remarkable performance on a range of
tasks. A key step to further empowering RNN-based approaches is improving their …

Mining time series motifs is a fundamental, yet expensive task in exploratory data analytics.
In this paper, we therefore propose a fast method to find the top-k motifs with probabilistic …

Recently, rule-based classification on multivariate time series (MTS) data has gained lots of
attention, which could improve the interpretability of classification. However, state-of-the-art …

Time series motif discovery (TSMD) refers to the task of identifying patterns that occur
multiple times (possibly with minor variations) in a time series. All existing methods for TSMD …

Feature mining is a challenging work in the field of multivariate time series (MTS) data
mining. Traditional methods suffer from three major issues. 1) Learned shapelets may …

A leitmotif is a recurring theme in literature, movies or music that carries symbolic
significance for the piece it is contained in. When this piece can be represented as a multi …

The increasing availability of temporal data poses a challenge to time-series and signal-
processing domains due to its high numerosity and complexity. Symbolic representation …

This paper introduces a new pattern mining task that considers aligning or joining a set of
time series based on an arbitrary number of subsequences (ie, patterns) with arbitrary …