6 citations · 18 across the 16 of their papers we have counts for
17 papers
A Behavior-Guided Online Probabilistic Forecasting Method for Electric vehicle Charging Loads
Chenghan Li, Qingxiang Liu, Yinliang Xu +1
Electric vehicle (EV) charging loads exhibit strong behavioral heterogeneity and temporal variability, posing significant challenges for online probabilistic forecasting under evol…
Perceive to Hypothesize, Verify to Ground: An Agentic Reasoning Framework for Open-World Geo-Localization
Yutian Jiang, Ruijie Li, Sisuo Lyu +4
Open-world geo-localization requires models to reason over ambiguous visual cues through multi-step reasoning and external knowledge grounding. While recent large vision-language m…
LiveHouse-TS: An Open-world Living Benchmark for Time Series Foundation Models
Haomin Wen, Ziyu Zhou, Qingxiang Liu +2
Time Series Foundation Models (TSFMs) have recently emerged as a highly promising paradigm for cross-domain zero-shot forecasting. However, existing evaluation protocols predominan…
Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecasting
Qingxiang Liu, Anqi Liang, Heng Wang +1
Federated learning has emerged as a promising paradigm for spatio-temporal forecasting (STF), enabling collaborative model training without sharing raw observations. Existing feder…
Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction
Qingxiang Liu, Anqi Liang, Zhuoyang Jiang +5
Predicting a user's next Point-of-Interest (POI) based on their historical check-in records is a fundamental task in location-based services. While recent methods incorporating lar…
LifeSide: Benchmarking Agents as Lifelong Digital Companions
Yuqian Wu, Zhijie Deng, Wei Chen +8
Lifelong digital companions must integrate cross-session cues, continually update their understanding of users, and adapt to shifting privacy boundaries. Existing evaluations fail…
Discrete Prototypical Memories for Federated Time Series Foundation Models
Liwei Deng, Qingxiang Liu, Xinhe Niu +5
Leveraging Large Language Models (LLMs) as federated learning (FL)-based time series foundation models offers a promising way to transfer the generalization capabilities of LLMs to…
TS-Memory: Plug-and-Play Memory for Time Series Foundation Models
Sisuo Lyu, Siru Zhong, Tiegang Chen +6
Time Series Foundation Models (TSFMs) achieve strong zero-shot forecasting through large-scale pre-training, but adapting them to downstream domains under distribution shift remain…
Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models
Qingxiang Liu, Zhiqing Cui, Xiaoliang Luo +7
The underperformance of existing multimodal large language models for time series reasoning lies in the absence of rationale priors that connect temporal observations to their down…
Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models
Zhiqing Cui, Binwu Wang, Qingxiang Liu +4
Large language models (LLM) have emerged as a promising avenue for time series forecasting, offering the potential to integrate multimodal data. However, existing LLM-based approac…
OccamVTS: Distilling Vision Models to 1% Parameters for Time Series Forecasting
Sisuo Lyu, Siru Zhong, Weilin Ruan +4
Time series forecasting is fundamental to diverse applications, with recent approaches leverage large vision models (LVMs) to capture temporal patterns through visual representatio…
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection
Qingxiang Liu, Xiaoliang Luo, Chenghao Liu +5
Unsupervised anomaly detection of multivariate time series is a challenging task, given the requirements of deriving a compact detection criterion without accessing the anomaly poi…
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting
Qingxiang Liu, Sheng Sun, Yuxuan Liang +6
Multiple federated learning (FL) methods are proposed for traffic flow forecasting (TFF) to avoid heavy-transmission and privacy-leaking concerns resulting from the disclosure of r…
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
Qingxiang Liu, Xu Liu, Chenghao Liu +2
Unlike natural language processing and computer vision, the development of Foundation Models (FMs) for time series forecasting is blocked due to data scarcity. While recent efforts…
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach
Qingxiang Liu, Sheng Sun, Yuxuan Liang +2
The existing federated learning (FL) methods for spatio-temporal forecasting fail to capture the inherent spatio-temporal heterogeneity, which calls for personalized FL (PFL) metho…
Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting
Qingxiang Liu, Sheng Sun, Min Liu +2
Traffic flow forecasting (TFF) is of great importance to the construction of Intelligent Transportation Systems (ITS). To mitigate communication burden and tackle with the problem…
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation
Zhiyuan Wu, Sheng Sun, Yuwei Wang +5
Federated learning (FL) is a privacy-preserving machine learning paradigm in which the server periodically aggregates local model parameters from clients without assembling their p…