In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31 (3): 606-
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …

Time series data are a collection of chronological observations which are generated by
several domains such as medical and financial fields. Over the years, different tasks such as …

The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …

Although time-series classification has many applications in healthcare and manufacturing,
the high cost of data collection and labeling hinders its widespread use. To reduce data …

The article addresses the problem of temporal data mining, in particular classification, with
support vector machines (SVM). If no application-specific knowledge about the nature of the …

Power utilities collect a large amount of metering data from substations and customers. This
data can provide insights for planning outages, making network investment decisions …

Sequence classification is essential for domains from medical diagnosis to online
advertising. In these settings, data are typically proprietary, and annotations are expensive …

This thesis studies and develops computational methods for efficient unsupervised learning
of robust feature representations from time series with the purpose of (i) simplifying and …

Time series classification (TSC) problem has been a significantly attractive research
problem for decades. A large number of models with various types of features have been …

Time series clustering is widely applied in various areas. Existing researches focus mainly
on distance measures between two time series, such as dynamic time war** (DTW) based …