About
Shengsheng Lin
Shengsheng Lin (林升升) is a Ph.D. student in Computer Science at South China University of Technology and is expected to graduate in June 2027. He is advised by Prof. Weiwei Lin, whose research focuses on cloud computing and big data.
His research lies at the intersection of time series forecasting and general-purpose AI. He focuses on efficient forecasting architectures, periodicity-aware modeling, and multivariate dependency learning, while also exploring large language models and multimodal methods for temporal data. His recent works include SparseTSF, CycleNet, SegRNN, and TQNet.
Research interests
- Efficient architectures for time series forecasting
- Periodicity-aware learning and temporal pattern analysis
- Multivariate dependency learning for realistic forecasting
- Large-scale foundation models for time series
- Multimodal learning across time series, text, and vision
Selected Papers
Representative Publications
SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters
Introduces cross-period sparse forecasting to reduce model parameters to as few as 1,000 while strengthening long-range dependency modeling, robustness, and generalization; now deployed in Didi ride-hailing demand-supply forecasting.
CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns
Explicitly models periodic patterns through a periodic-residual decomposition design, improving periodic utilization, residual modeling, and overall interpretability.
Temporal Query Network for Efficient Multivariate Time Series Forecasting
Introduces temporal queries to capture global periodic patterns, enabling more robust multivariate dependency learning and stronger multivariate forecasting performance.
Education
Academic Training
South China University of Technology
Sep. 2022 - Present
Ph.D. Student in Computer Science
Conducting research on deep learning and artificial intelligence, with a particular focus on time series forecasting, efficient modeling, and periodic pattern analysis.
South China University of Technology
Sep. 2018 - Jun. 2022
B.E. Student in Computer Science
Built a systematic foundation in computer science, including computer systems, computer networks, software engineering, and core algorithmic training.
Experience
Industry and Engineering Practice
Didi
Dec. 2025 - Apr. 2026
Elite Program Algorithm Intern, Supply-Demand Scheduling Strategy Team
Optimized supply-demand forecasting models based on SparseTSF to address the high complexity of temporal dependency modeling in the original production system. Improved core business forecasting accuracy by 7%, reduced parameter count by 90%, and accelerated training by 5x, substantially speeding up model iteration and deployment.
Tencent
Jul. 2021 - Sep. 2021
Backend Development Intern, R&D Management Department
Developed backend services in Go using Gin, Docker, Nginx, Redis, Kafka, and XORM. Built and maintained scheduled-task interfaces and developed CLI tools with Cobra for unified task management.
Awards
Honors
- National Scholarship (Ph.D. Student)
- President Scholarship, South China University of Technology
- National Scholarship (Undergraduate)
- China International College Students' Innovation Competition Gold Award