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 …

Deep learning, a crucial technique for achieving artificial intelligence (AI), has been
successfully applied in many fields. The gradual application of the latest architectures of …

Although pre-trained transformers and reprogrammed text-based LLMs have shown strong
performance on time series tasks, the best-performing architectures vary widely across …

Time-series data exists in every corner of real-world systems and services, ranging from
satellites in the sky to wearable devices on human bodies. Learning representations by …

Many cybersecurity problems that require real-time decision-making based on temporal
observations can be abstracted as a sequence modeling problem, eg, network intrusion …

In recent years, Contrastive Learning (CL) has become a predominant representation
learning paradigm for time series. Most existing methods manually build specific CL …

Effective epidemic forecasting is critical for public health strategies and efficient medical
resource allocation, especially in the face of rapidly spreading infectious diseases. However …

The real-world Electronic Health Records (EHRs) present irregularities due to changes in
the patient's health status, resulting in various time intervals between observations and …

In this article, the topic of time series modelling is discussed. It highlights the criticality of
analysing and forecasting time series data across various sectors, identifying five primary …

Existing approaches for learning representations of time-series keep the temporal
arrangement of the time-steps intact with the presumption that the original order is the most …