Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

This paper brings deep learning at the forefront of research into time series classification
(TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of …

Automated classification of supernovae (SNe) based on optical photometric light-curve
information is essential in the upcoming era of wide-field time domain surveys, such as the …

In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are
relatively uncommon objects compared to other classes of variable events. Along with this …

The improvement of observation capabilities has expanded the scale of new data available
for time domain astronomy research, and the accumulation of observational data continues …

Deep neural networks (DNNs) have achieved state-of-the-art results on time series
classification (TSC) tasks. In this work, we focus on leveraging DNNs in the often …

Despite the utility of neural networks (NNs) for astronomical time-series classification, the
proliferation of learning architectures applied to diverse data sets has thus far hampered a …

Time series classification and retrieval are two important tasks of time series analysis.
Existing methods solve these two tasks separately, which ignores the sharable information …

In cold regions, ice jams frequently result in severe flooding due to a rapid rise in water
levels upstream of the jam. Sudden floods resulting from ice jams threaten human safety and …

Modern power systems are experiencing the challenge of high uncertainty with the
increasing penetration of renewable energy resources and the electrification of heating …