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 …

Multivariate time series (MTS) forecasting is an urgent problem for numerous valuable
applications. At present, attention-based methods can relieve recurrent neural networks' …

Multi-horizon forecasting of multivariate time series has always been a prominent research
topic in domains such as finance and transportation. While prediction models that integrate …

Multivariate time series long-term forecasting has always been the subject of research in
various fields such as economics, finance, and traffic. In recent years, attention-based …

Forecasting influenza in a timely manner aids health organizations and policymakers in
adequate preparation and decision making. However, effective influenza forecasting still …

Multi-step forecasting of multivariate time series plays a critical role in many fields, such as
disaster warning and financial analysis. While attention-based recurrent neural networks …

Multivariate time series forecasting is widely used in a variety of fields, such as cyber-
physical systems and financial market analysis. Recently, attention-based recurrent neural …

Multivariate time series prediction is helpful for scientific decision-making and reliable
assessments in numerous fields. Capturing time series nonlinear change rules with …

Chlorophyll forecasting is helpful for understanding characteristics of red tides, thus
enabling early warning. In practice, it is formulated as a time series forecasting problem …

Although attention-based encoder–decoder models achieve encouraging performance in
multivariate time series multi-horizon forecasting, two key limitations exist in current …