Due to the high data volume and non-stationarity of time series data, it is very difficult to
directly use the original data for anomaly detection. In this study, a novel framework of …

With the development of electronic science and technology, green landscape planning and
design in urban landscape design has become a new trend and direction. The emergence …

As a nonlinear distance measurement, Dynamic time warping (DTW) is widely used to solve
the alignment problem of time series. However, due to local distortion of signal, noise …

The ordinal pattern is an essential tool to extract the information in time series. However,
little attention has been paid to the transition probability matrix of ordinal patterns, which …

Recently there has been an increase in the studies on time-series data mining specifically
time-series clustering due to the vast existence of time-series in various domains. The large …

Time series classification is an important and challenging problem in data analysis. With the
increase in time series data availability, hundreds of algorithms have been proposed. A …

Some of the most important information on a radio pulsar is derived from its average pulse
profile. Many early pulsar studies were necessarily based on only a few such profiles. In …

Similarity search is a foundational task in time series data mining. Although there are many
ways to measure the similarity of time series, a lot of evidence indicates that dynamic time …

Purpose The common methods for clustering time series are the use of specific distance
criteria or the use of standard clustering algorithms. Ensemble clustering is one of the …

The diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The
rail profile faults are the most direct impact factors when considering the health conditions of …