Unsupervised representation learning approaches aim to learn discriminative feature
representations from unlabeled data, without the requirement of annotating every sample …

This paper deals with supervised classification of multivariate time series. In particular, the
goal is to propose a filter method to select a subset of time series. Consequently, we adopt …

The motivation of this paper is to examine the effectiveness of state-of-the-art and newly
proposed motion capture pattern recognition methods in the task of head gesture …

With large deployment of wireless sensor networks, anomaly detection for sensor data is
becoming increasingly important in various fields. As a vital data form of sensor data, time …

To improve the reliability and reduce power loss of distribution network, the dynamic
reconfiguration is widely used. It is employed to find an optimal topology for each time …

Symbolic Aggregate approXimation (SAX) is an efficient symbolic representation method
that has been widely used in time series data mining. Its major limitation is that it relies …

Representing time-series without relying on the domain knowledge and independent of the
end-task is a challenging problem. The same situation applies to trajectory data as well …

In the electrical system, the wind power is generally measured every 15 min, resulting in
35040 time series datain one year. It will be a time-consuming task to calculate the …

Time series analysis has become an important field of data mining in the last decade.
Dynamics of real-world processes are important in domains like seismology, medicine …

Time series analysis has become an important field of data mining in the last decade.
Dynamics of real-world processes are important in domains like seismology, medicine …