Time series (TS) are sequences of values ordered in time. Such TS have in common, that
important insights from the data can be drawn by inspecting local substructures, and not the …

Motif Discovery consists of finding repeated patterns and locating their occurrences in a time
series without prior knowledge about their shape or location. Most state-of-the-art algorithms …

Subsequence anomaly (or outlier) detection in long sequences is an important problem with
applications in a wide range of domains. However, the approaches that have been …

In the last fifteen years, data series motif discovery has emerged as one of the most useful
primitives for data series mining, with applications to many domains, including robotics …

In the last 15 years, data series motif and discord discovery have emerged as two useful and
well-used primitives for data series mining, with applications to many domains, including …

The stock market investors aim at maximizing their investment returns. Stock
recommendation task is to recommend stocks with higher return ratios for the investors. Most …

Many modern applications produce massive amounts of data series that need to be
analyzed, requiring efficient similarity search operations. However, the state-of-the-art data …

The problems of recurrent and anomalous pattern discovery in time series, eg, motifs and
discords, respectively, have received a lot of attention from researchers in the past decade …

A time series motif intuitively is a short time series that repeats itself approximately the same
within a larger time series. Such motifs often represent concealed structures, such as heart …

Data series similarity search is a core operation for several data series analysis applications
across many different domains. However, the state-of-the-art techniques fail to deliver the …