221 citations · 290 across the 26 of their papers we have counts for
31 papers
Gated Slot Attention-2: Two-Sided Associative Memory Correction in Linear Attention
Ruijie Li, Shengnan Ding, Weimin Zhang +6
Linear attention models have emerged as efficient alternatives to standard attention, but effectively managing their fixed-size recurrent memory remains challenging. To improve mem…
PREM: Prefix-Steered Recurrent Memory for Long-Video Understanding
Siru Zhong, Qiongyan Wang, Xiaohui Lv +5
Long-video understanding must capture transient visual evidence under strict token budgets, yet existing methods compress frames, append memory tokens, or alter internal key-value…
Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting
Siru Zhong, Zhao Meng, Haohuan Fu +3
Local temporal patterns in real-world time series continuously shift, rendering globally shared transformations suboptimal. Current deep forecasting models, despite their scale and…
Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting
Zhiqing Cui, Siru Zhong, Ming Jin +3
Global air quality forecasting grapples with extreme spatial heterogeneity and the poor generalization of existing transductive models to unseen regions. To tackle this, we propose…
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…
DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting
Siru Zhong, Yiqiu Liu, Zhiqing Cui +4
Deep time series models are vulnerable to noisy data ubiquitous in real-world applications. Existing robustness strategies either prune data or rely on costly prior quantification,…
ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models
Bosong Huang, Ming Jin, Yuxuan Liang +5
Explaining time series classification models is crucial, particularly in high-stakes applications such as healthcare and finance, where transparency and trust play a critical role.…
Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting
Tianxiang Zhan, Ming Jin, Yuanpeng He +2
Recurring concept drift is pervasive in real-world online time series, where the underlying data-generating process repeatedly alternates between a small set of regimes, most notab…
Comba: Improving Bilinear RNNs with Closed-loop Control
Jiaxi Hu, Yongqi Pan, Jusen Du +5
Recent efficient sequence modeling methods such as Gated DeltaNet, TTT, and RWKV-7 have achieved performance improvements by supervising the recurrent memory management through Del…
Advanced Long-term Earth System Forecasting
Hao Wu, Yuan Gao, Ruijian Gou +30
Reliable long-term forecasting of Earth system dynamics is fundamentally limited by instabilities in current artificial intelligence (AI) models during extended autoregressive simu…
Multi-Order Wavelet Derivative Transform for Deep Time Series Forecasting
Ziyu Zhou, Jiaxi Hu, Qingsong Wen +2
In deep time series forecasting, the Fourier Transform (FT) is extensively employed for frequency representation learning. However, it often struggles in capturing multi-scale, tim…
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models
Xu Liu, Taha Aksu, Juncheng Liu +7
Time series analysis is crucial for understanding dynamics of complex systems. Recent advances in foundation models have led to task-agnostic Time Series Foundation Models (TSFMs)…
Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey
Yuxuan Liang, Haomin Wen, Yutong Xia +6
Spatio-Temporal (ST) data science, which includes sensing, managing, and mining large-scale data across space and time, is fundamental to understanding complex systems in domains s…
Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting
Siru Zhong, Weilin Ruan, Ming Jin +3
Recent advancements in time series forecasting have explored augmenting models with text or vision modalities to improve accuracy. While text provides contextual understanding, it…
Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation
Yaxuan Kong, Yiyuan Yang, Shiyu Wang +7
Understanding time series data is fundamental to many real-world applications. Recent work explores multimodal large language models (MLLMs) to enhance time series understanding wi…
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…
Toward Physics-guided Time Series Embedding
Jiaxi Hu, Bowen Zhang, Qingsong Wen +2
In various scientific and engineering fields, the primary research areas have revolved around physics-based dynamical systems modeling and data-driven time series analysis. Accordi…
Towards Neural Scaling Laws for Time Series Foundation Models
Qingren Yao, Chao-Han Huck Yang, Renhe Jiang +3
Scaling laws offer valuable insights into the design of time series foundation models (TSFMs). However, previous research has largely focused on the scaling laws of TSFMs for in-di…
Cluster-Wide Task Slowdown Detection in Cloud System
Feiyi Chen, Yingying Zhang, Lunting Fan +4
Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomal…
Unlocking the Power of LSTM for Long Term Time Series Forecasting
Yaxuan Kong, Zepu Wang, Yuqi Nie +5
Traditional recurrent neural network architectures, such as long short-term memory neural networks (LSTM), have historically held a prominent role in time series forecasting (TSF)…
TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting
Jiaxi Hu, Qingsong Wen, Sijie Ruan +2
Recently, multivariate time series forecasting tasks have garnered increasing attention due to their significant practical applications, leading to the emergence of various deep fo…
TSI-Bench: Benchmarking Time Series Imputation
Wenjie Du, Jun Wang, Linglong Qian +12
Effective imputation is a crucial preprocessing step for time series analysis. Despite the development of numerous deep learning algorithms for time series imputation, the communit…
Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
Jiaxi Hu, Disen Lan, Ziyu Zhou +2
State Space Models (SSMs) have emerged as a potent tool in sequence modeling tasks in recent years. These models approximate continuous systems using a set of basis functions and d…
A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
Yiyuan Yang, Ming Jin, Haomin Wen +9
Diffusion models have been widely used in time series and spatio-temporal data, enhancing generative, inferential, and downstream capabilities. These models are applied across dive…