Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …

Streaming data are increasingly present in real-world applications such as sensor
measurements, satellite data feed, stock market, and financial data. The main characteristics …

Most methods for time series classification that attain state-of-the-art accuracy have high
computational complexity, requiring significant training time even for smaller datasets, and …

The UCR time series archive–introduced in 2002, has become an important resource in the
time series data mining community, with at least one thousand published papers making use …

Capturing the dynamical properties of time series concisely as interpretable feature vectors
can enable efficient clustering and classification for time-series applications across science …

Multivariate time series are used in many science and engineering domains, including
health-care, astronomy, and high-performance computing. A recent trend is to use machine …

Early classification of time series has been extensively studied for minimizing class
prediction delay in time-sensitive applications such as medical diagnostic and industrial …

This work considers the problem of computing distances between structured objects such as
undirected graphs, seen as probability distributions in a specific metric space. We consider a …

Automated machine learning has the ability to improve the efficiency of time series
classification due to the ability to combine multiple feature extraction methods and …

Time series classification is a subfield of machine learning with numerous real-life
applications. Due to the temporal structure of the input data, standard machine learning …