Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

Nowadays, multi-sensor technologies are applied in many fields, eg, Health Care (HC),
Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can …

Deep learning (DL) based methods have swept the field of mechanical fault diagnosis,
because of the powerful ability of feature representation. However, many of existing DL …

The scarcity of labeled data is one of the main challenges of applying deep learning models
on time series data in the real world. Therefore, several approaches, eg, transfer learning …

Time series data usually contains local and global patterns. Most of the existing feature
networks focus on local features rather than the relationships among them. The latter is also …

Multivariate time series classification is one of the increasingly important issues in machine
learning. Existing methods focus on establishing the global long-range dependencies or …

Enhancing the expressive capacity of deep learning-based time series models with self-
supervised pre-training has become ever-increasingly prevalent in time series classification …

The receptive field (RF), which determines the region of time series to be “seen” and used, is
critical to improve the performance for time series classification (TSC). However, the …

Stock price trend prediction is to seek profit maximum of stock investment by estimating
future stock price tendency. Nevertheless, it is still a tough task due to noisy and non …

Accessible high-quality data is the bread and butter of machine learning research, and the
demand for data has exploded as larger and more advanced ML models are built across …