In almost every scientific field, measurements are performed over time. These observations
lead to a collection of organized data called time series. The purpose of time-series data …

Many high level representations of time series have been proposed for data mining,
including Fourier transforms, wavelets, eigenwaves, piecewise polynomial models, etc …

Time series analysis is a key technique for extracting and predicting events in domains as
diverse as epidemiology, genomics, neuroscience, environmental sciences, economics, and …

In this work our approach for human gesture recognition with inertial sensors is presented.
The proposed method utilizes a dynamic time warping (DTW) algorithm for online time …

Time series motif discovery is an important problem with applications in a variety of areas
that range from telecommunications to medicine. Several algorithms have been proposed to …

Time series motifs are sets of very similar subsequences of a long time series. They are of
interest in their own right, and are also used as inputs in several higher-level data mining …

Motifs are the most repetitive/frequent patterns of a time-series. The discovery of motifs is
crucial for practitioners in order to understand and interpret the phenomena occurring in …

Classification of time series is an important task with many challenging applications like
brain wave (EEG) analysis, signature verification or speech recognition. In this paper we …

Abstract Time Series Data mining is a popular field in data science to discover and extract
useful information from the time series data. Time Series Motif discovery is one of the tasks …

A motif is a pair of non-overlapping sequences with very similar shapes in a time series. We
study the online top-k most similar motif discovery problem. A special case of this problem …