37 citations · 90 across the 18 of their papers we have counts for
22 papers
World2TS: Real-World Event Context for Time-Series Forecasting
Zhiding Liu, Jiqian Yang, Mingyue Cheng +2
Time-series forecasting plays a vital role in a wide range of real-world applications. Recent approaches have achieved strong performance by modeling temporal correlations and cros…
CastFlow: Bridging Evidence Acquisition and Accurate Prediction in Agentic Time Series Forecasting
Bokai Pan, Mingyue Cheng, Zhiding Liu +6
Time series forecasting is a fundamental task in temporal data analytics, and large language model (LLM)-based agents offer a promising paradigm through autonomous tool interaction…
StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser
Jintao Zhang, Zirui Liu, Mingyue Cheng +3
Diffusion models have been used for probabilistic time series forecasting and show strong potential. However, fixed noise schedules often produce intermediate states that are hard…
KuaiSearch: An E-Commerce Search Dataset with Authentic Queries and Product Texts for Recall, Ranking, and Relevance
Yupeng Li, Ben Chen, Mingyue Cheng +7
E-commerce search connects user needs with massive product inventories, yet real-world systems face challenges from ambiguous queries, noisy product texts, and diverse user prefere…
InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancement
Mingyue Cheng, Xiaoyu Tao, Huajian Zhang +5
Most existing time series classification methods adopt a discriminative paradigm that maps input sequences directly to one-hot encoded class labels. While effective, this paradigm…
OneCast: Structured Decomposition and Modular Generation for Cross-Domain Time Series Forecasting
Tingyue Pan, Mingyue Cheng, Shilong Zhang +5
Cross-domain time series forecasting is a valuable task in various web applications. Despite its rapid advancement, achieving effective generalization across heterogeneous time ser…
Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce
Zhiding Liu, Ben Chen, Mingyue Cheng +6
Search-based recommendation is one of the most critical application scenarios in e-commerce platforms. Users' complex search contexts--such as spatiotemporal factors, historical in…
FlowRec: Prior-Informed Flow Matching for Efficient Sequential Recommendation Generation
Li Li, Mingyue Cheng, Yuyang Ye +2
Sequential recommendation aims to predict each user's next preferred item based on their historical interactions. Recently, diffusion-based approaches have demonstrated strong gene…
Improving Time Series Forecasting via Instance-aware Post-hoc Revision
Zhiding Liu, Mingyue Cheng, Guanhao Zhao +3
Time series forecasting plays a vital role in various real-world applications and has attracted significant attention in recent decades. While recent methods have achieved remarkab…
Unveiling the Magic of Code Reasoning through Hypothesis Decomposition and Amendment
Yuze Zhao, Tianyun Ji, Wenjun Feng +6
The reasoning abilities are one of the most enigmatic and captivating aspects of large language models (LLMs). Numerous studies are dedicated to exploring and expanding the boundar…
DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting
Zhiding Liu, Jiqian Yang, Qingyang Mao +5
Multivariate time series forecasting plays a crucial role in various real-world applications. Significant efforts have been made to integrate advanced network architectures and tra…
Conditional Denoising Meets Polynomial Modeling: A Flexible Decoupled Framework for Time Series Forecasting
Jintao Zhang, Mingyue Cheng, Xiaoyu Tao +2
Time series forecasting models are becoming increasingly prevalent due to their critical role in decision-making across various domains. However, most existing approaches represent…
TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation
Daoyu Wang, Mingyue Cheng, Zhiding Liu +1
Self-supervised learning has garnered increasing attention in time series analysis for benefiting various downstream tasks and reducing reliance on labeled data. Despite its effect…
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation
Li Li, Mingyue Cheng, Zhiding Liu +3
Sequential recommendation models user interests based on historical behaviors to provide personalized recommendation. Previous sequential recommendation algorithms primarily employ…
A Hybrid Multi-Factor Network with Dynamic Sequence Modeling for Early Warning of Intraoperative Hypotension
Mingyue Cheng, Jintao Zhang, Zhiding Liu +1
Intraoperative hypotension (IOH) prediction using past physiological signals is crucial, as IOH may lead to inadequate organ perfusion and significantly elevate the risk of severe…
Advancing Time Series Classification with Multimodal Language Modeling
Mingyue Cheng, Yiheng Chen, Qi Liu +2
For the advancements of time series classification, scrutinizing previous studies, most existing methods adopt a common learning-to-classify paradigm - a time series classifier mod…
Generative Pretrained Hierarchical Transformer for Time Series Forecasting
Zhiding Liu, Jiqian Yang, Mingyue Cheng +2
Recent efforts have been dedicated to enhancing time series forecasting accuracy by introducing advanced network architectures and self-supervised pretraining strategies. Neverthel…
Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems
Hao Zhang, Mingyue Cheng, Zhiding Liu +1
Sequential recommender systems (SRS) have gained increasing popularity due to their remarkable proficiency in capturing dynamic user preferences. In the current setup of SRS, a com…
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling
Junzhe Jiang, Shang Qu, Mingyue Cheng +8
Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the…
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
Mingyue Cheng, Xiaoyu Tao, Zhiding Liu +4
Learning transferable representations from unlabeled time series is crucial for improving performance in data-scarce classification. Existing self-supervised methods often operate…
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification
Mingyue Cheng, Qi Liu, Zhiding Liu +3
Deep learning-based algorithms, e.g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task. Nevertheless, they suffer from the…
One Person, One Model--Learning Compound Router for Sequential Recommendation
Zhiding Liu, Mingyue Cheng, Zhi Li +2
Deep learning has brought significant breakthroughs in sequential recommendation (SR) for capturing dynamic user interests. A series of recent research revealed that models with mo…