11 citations · 17 across the 33 of their papers we have counts for
37 papers
XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching
Ziyang Zhang, Hanyin Cheng, Xiangfei Qiu +3
Future exogenous variables provide valuable information for forecasting endogenous time series. Existing covariate-aware methods primarily learn the direct influence of exogenous v…
HALO: Enhancing Time Series Generation via Hyperspherical Latents and Masked AutoregRessive Modeling
Chunyi Hou, Xiangfei Qiu, Hanyin Cheng +2
Most existing time series generators rely on a two-stage modeling paradigm: the first stage learns discrete latent representations of time series; the second stage performs autoreg…
BITS: Rethinking Fair and Comprehensive Evaluation for Irregular Time Series Forecasting
Kangjia Yan, Linfeng Wang, Tianen Shen +7
Despite recent progress in irregular time series forecasting, the field still lacks a unified benchmark for fair and comprehensive evaluation. Existing evaluations are often conduc…
WPBench: A Comprehensive Benchmark for Wind Power Forecasting
Yuhan Zhu, Jilin Hu, Xinying Cai +7
Accurate, reliable, and deployable wind power forecasting is critical for power system dispatch, renewable energy integration, and electricity market operations. Progress in this f…
QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization
Yujie Li, Zezhi Shao, Chengqing Yu +7
Ubiquitous time series data across diverse domains enables critical applications in areas such as transportation systems and power grids. Recently, training foundation models on ma…
Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework
Tianen Shen, Zhengyu Li, Yutong Li +4
Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing. Their core challenge…
UniWind: Day-Ahead Wind Power Forecasting via Physics-Informed State Routing
Ronghui Xu, Tongxin Wu, Guozhen Zhang +4
Accurate day-ahead wind power forecasting is essential for cost-effective power-system operation. Wind power generation is primarily driven by future meteorological conditions whil…
HyFAD: Hybrid Time-Frequency Diffusion with Frequency-Aware Embedding for Time Series Imputation
Hongfan Gao, Wangmeng Shen, Bin Yang +1
Diffusion models have demonstrated strong performance in time series modeling due to their ability to progressively capture complex data distributions through iterative denoising.…
Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models
Xingjian Wu, Junkai Lu, Siyu Yan +4
Recent advances in Large Language Models (LLMs) have catalyzed the development of multi-agent systems (MAS) for complex reasoning tasks. However, existing MAS typically rely on pre…
GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables
Zhengyu Li, Xiangfei Qiu, Yuhan Zhu +4
Exogenous variables offer valuable supplementary information for predicting future endogenous variables. Forecasting with exogenous variables needs to consider both past-to-future…
CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter
Hanyin Cheng, Xingjian Wu, Yang Shu +4
Most existing Time Series Foundation Models (TSFMs) use channel independent modeling and focus on capturing and generalizing temporal dependencies, while neglecting the correlation…
Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction
Shiyan Hu, Jianxin Jin, Yang Shu +3
Time series anomaly detection plays a critical role in many dynamic systems. Despite its importance, previous approaches have primarily relied on unimodal numerical data, overlooki…
MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs
Zhi Lei, Chenxi Liu, Hao Miao +3
Irregularly sampled time series (ISTS) are widespread in real-world scenarios, exhibiting asynchronous observations on uneven time intervals across diverse variables. Existing ISTS…
LightGTS-Cov: Covariate-Enhanced Time Series Forecasting
Yong Shang, Zhipeng Yao, Ning Jin +3
Time series foundation models are typically pre-trained on large, multi-source datasets; however, they often ignore exogenous covariates or incorporate them via simple concatenatio…
SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacement
Xiangfei Qiu, Xvyuan Liu, Tianen Shen +4
Time series forecasting is important in many fields that require accurate predictions for decision-making. Patching techniques, commonly used and effective in time series modeling,…
TimeART: Towards Agentic Time Series Reasoning via Tool-Augmentation
Xingjian Wu, Junkai Lu, Zhengyu Li +5
Time series data widely exist in real-world cyber-physical systems. Though analyzing and interpreting them contributes to significant values, e.g, disaster prediction and financial…
FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting
Xingjian Wu, Hanyin Cheng, Xiangfei Qiu +4
In this work, we introduce FLAME, a family of extremely lightweight and capable Time Series Foundation Models, which support versatile forecasting tasks via generative probabilisti…
DBLoss: Decomposition-based Loss Function for Time Series Forecasting
Xiangfei Qiu, Xingjian Wu, Hanyin Cheng +4
Time series forecasting holds significant value in various domains such as economics, traffic, energy, and AIOps, as accurate predictions facilitate informed decision-making. Howev…
Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective
Xingjian Wu, Xiangfei Qiu, Hanyin Cheng +4
Time Series Forecasting has made significant progress with the help of Patching technique, which partitions time series into multiple patches to effectively retain contextual seman…
ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting
Jindong Tian, Yifei Ding, Ronghui Xu +3
Weather forecasting is a fundamental task in spatiotemporal data analysis, with broad applications across a wide range of domains. Existing data-driven forecasting methods typicall…
Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting
Xiangfei Qiu, Liu Yang, Xiangyu Xu +9
Time series forecasting occurs in a range of financial applications providing essential decision-making support to investors, regulatory institutions, and analysts. Unlike multivar…
ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting
Xvyuan Liu, Xiangfei Qiu, Hanyin Cheng +4
Irregular multivariate time series (IMTS) are prevalent in critical domains like healthcare and finance, where accurate forecasting is vital for proactive decision-making. However,…
Aurora: Towards Universal Generative Multimodal Time Series Forecasting
Xingjian Wu, Jianxin Jin, Wanghui Qiu +4
Cross-domain generalization is very important in Time Series Forecasting because similar historical information may lead to distinct future trends due to the domain-specific charac…
Task-Aware Mixture-of-Experts for Time Series Analysis
Xingjian Wu, Zhengyu Li, Hanyin Cheng +4
Time Series Analysis is widely used in various real-world applications such as weather forecasting, financial fraud detection, imputation for missing data in IoT systems, and class…