Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …

Long short-term memory fully convolutional neural networks (LSTM-FCNs) and Attention
LSTM-FCN (ALSTM-FCN) have shown to achieve the state-of-the-art performance on the …

Nowadays, with the rapid growth of the internet of things (IoT), massive amounts of time
series data are being generated. Time series data play an important role in scientific and …

In this paper, a novel trajectory clustering algorithm-TAD-is proposed to extract trajectory
Stays based on spatial-temporal density analysis of data. Two new metrics-NMAST …

Visual analytics for time series data has received a considerable amount of attention.
Different approaches have been developed to understand the characteristics of the data and …

In this paper, we apply a functional clustering method to the multivariate time series of life
expectancy at birth of the female populations collected in the Human Mortality Database. We …

In finance, cluster analysis is a tool particularly useful for classifying stock market
multivariate time series data related to daily returns, volatility daily stocks returns, commodity …

Time series modeling for predictive purpose has been an active research area of machine
learning for many years. However, no sufficiently comprehensive and meanwhile …

Clustering of multivariate time series is a central problem in data mining with applications in
many fields. Frequently, the clustering target is to identify groups of series generated by the …

Time series data are commonly clustered based on their distributional characteristics. The
moments play a central role among such characteristics because of their relevant …