Multivariate time series forecasting (MTSF) is crucial for decision-making to precisely
forecast the future values/trends, based on the complex relationships identified from …

Forecasting of Irregular Multivariate Time Series (IMTS) is critical for numerous areas, such
as healthcare, biomechanics, climate science, and astronomy. Despite existing research …

In this paper, we propose a BERT-style selfsupervised learning model, VQ-MTM (Vector
Quantization Masked Time-Series Modeling), for the EEG time series data analysis. At its …

Time series forecasting is a critical task that provides key information for decision-making
across various fields. Recently, various fundamental deep learning architectures such as …

Multivariate time series forecasting is of central importance in modern intelligent decision
systems. The dynamics of multivariate time series are jointly characterized by temporal …

Predicting the cooling capacity of converter valves is crucial for maintaining the stability and
efficiency of high-voltage direct current (HVDC) systems. This task involves handling …

Photovoltaic (PV) power generation forecasting is an important method to solve the inherent
volatility and inter-mittency of solar energy. However, traditional research methods often …

Multivariate time series data are integral to numerous real-world applications, including
finance, healthcare, and meteorology, where accurate forecasting is paramount for informed …

In this paper, we propose a BERT-style self-supervised learning model, VQ-MTM (Vector
Quantization Masked Time-Series Modeling), for the EEG time series data analysis. At its …