2 citations · 4 across the 8 of their papers we have counts for
7 papers · 1 filter
Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology
Bin Wang, Shuo Lian, Yuanyuan Hou +5
Depression screening from large-scale behavioral data is challenged by fragmented circadian indicators, limited interpretability, and the lack of intervention-oriented analysis. Ex…
Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models
Attia Qammar, Qazi Haseeb Yousaf, Ali Azam +3
Climate change, primarily driven by carbon dioxide (CO2) emissions, requires accurate forecasting tools to support effective mitigation policies and sustainable development strateg…
Non-collective Calibrating Strategy for Time Series Forecasting
Bin Wang, Yongqi Han, Minbo Ma +4
Deep learning-based approaches have demonstrated significant advancements in time series forecasting. Despite these ongoing developments, the complex dynamics of time series make i…
CoIFNet: A Unified Framework for Multivariate Time Series Forecasting with Missing Values
Kai Tang, Ji Zhang, Hua Meng +5
Multivariate time series forecasting (MTSF) is a critical task with broad applications in domains such as meteorology, transportation, and economics. Nevertheless, pervasive missin…
Causally-Aware Unsupervised Feature Selection Learning
Zongxin Shen, Yanyong Huang, Dongjie Wang +3
Unsupervised feature selection (UFS) has recently gained attention for its effectiveness in processing unlabeled high-dimensional data. However, existing methods overlook the intri…
UMOD: A Novel and Effective Urban Metro Origin-Destination Flow Prediction Method
Peng Xie, Minbo Ma, Bin Wang +2
Accurate prediction of metro Origin-Destination (OD) flow is essential for the development of intelligent transportation systems and effective urban traffic management. Existing ap…
Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting
Qi Xiong, Kai Tang, Minbo Ma +3
Long-term time series forecasting (LTSF) is a critical task across diverse domains. Despite significant advancements in LTSF research, we identify a performance bottleneck in exist…