Time series data is increasingly used in a wide range of fields, and it is often relied on in
crucial applications and high-stakes decision-making. For instance, sensors generate time …

The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …

Anomaly detection for time-series data is crucial in the management of systems for
streaming applications, computational services, and cloud platforms. The majority of current …

In the last decades, data-driven methods have gained great popularity in the industry,
supported by state-of-the-art advancements in machine learning. These methods require a …

Objective: Lesion detection with positron emission tomography (PET) imaging is critical for
tumor staging, treatment planning, and advancing novel therapies to improve patient …

Learning complex time series forecasting models usually requires a large amount of data, as
each model is trained from scratch for each task/data set. Leveraging learning experience …

Shapelets are subsequences that are effective for classifying time-series instances.
Learning shapelets by a continuous optimization has recently been studied to improve …

Time series classifiers are not only challenging to design, but they are also notoriously
difficult to deploy for critical applications because end users may not understand or trust …

With the continuous increase of deep learning applications in safety critical systems, the
need for an interpretable decision-making process has become a priority within the research …

We propose to embed time series in a latent space where pairwise Euclidean distances
(EDs) between samples are equal to pairwise dissimilarities in the original space, for a given …