2 citations · 2 across the 9 of their papers we have counts for
9 papers
EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection
Lei Jiang, Ye Wei, Xinyu Xi +7
Financial time series exhibit non-stationary and heterogeneous statistical properties, making change-point detection challenging because no single unsupervised algorithm performs c…
MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition
Yifan Bao, Xinyu Xi, Xinyu Liu +8
Automated data science is a structured model-selection problem. A solution must choose data transformations, feature representations, architecture, training procedure, evaluation p…
Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data
Lehao Li, Qiang Huang, Yihao Ang +3
Clustering mixed-type tabular data is fundamental for exploratory analysis, yet remains challenging due to misaligned numerical-categorical representations, uneven and context-depe…
RFOD: Random Forest-based Outlier Detection for Tabular Data
Yihao Ang, Peicheng Yao, Yifan Bao +4
Outlier detection in tabular data is crucial for safeguarding data integrity in high-stakes domains such as cybersecurity, financial fraud detection, and healthcare, where anomalie…
Structured Agentic Workflows for Financial Time-Series Modeling with LLMs and Reflective Feedback
Yihao Ang, Yifan Bao, Lei Jiang +4
Time-series data is central to decision-making in financial markets, yet building high-performing, interpretable, and auditable models remains a major challenge. While Automated Ma…
CTBench: Cryptocurrency Time Series Generation Benchmark
Yihao Ang, Qiang Wang, Qiang Huang +5
Synthetic time series are essential tools for data augmentation, stress testing, and algorithmic prototyping in quantitative finance. However, in cryptocurrency markets, characteri…
Towards Controllable Time Series Generation
Yifan Bao, Yihao Ang, Qiang Huang +2
Time Series Generation (TSG) has emerged as a pivotal technique in synthesizing data that accurately mirrors real-world time series, becoming indispensable in numerous applications…
TSGBench: Time Series Generation Benchmark
Yihao Ang, Qiang Huang, Yifan Bao +2
Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data augmentation, anomaly detection, and privacy preservation. Although significant strides…
Panoptic Scene Graph Generation
Jingkang Yang, Yi Zhe Ang, Zujin Guo +3
Existing research addresses scene graph generation (SGG) -- a critical technology for scene understanding in images -- from a detection perspective, i.e., objects are detected usin…