102 citations · 322 across the 78 of their papers we have counts for
27 papers · 1 filter
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…
Towards Non-Stationary Time Series Forecasting with Temporal Stabilization and Frequency Differencing
Junkai Lu, Peng Chen, Chenjuan Guo +3
Time series forecasting is critical for decision-making across dynamic domains such as energy, finance, transportation, and cloud computing. However, real-world time series often e…
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…
STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adapter
Hanyin Cheng, Ruitong Zhang, Yuning Lu +5
While Time Series Foundation Models (TSFMs) have demonstrated remarkable success in Multivariate Time Series Anomaly Detection (MTSAD), however, in real-world industrial scenarios,…
CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling
Beibu Li, Qichao Shentu, Yang Shu +5
Time series anomaly detection plays a crucial role in a wide range of real-world applications. Given that time series data can exhibit different patterns at different sampling gran…
SwiftTS: A Swift Selection Framework for Time Series Pre-trained Models via Multi-task Meta-Learning
Tengxue Zhang, Biao Ouyang, Yang Shu +3
Pre-trained models exhibit strong generalization to various downstream tasks. However, given the numerous models available in the model hub, identifying the most suitable one by in…
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…
Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising
Kangjia Yan, Chenxi Liu, Hao Miao +4
Effective time series forecasting enables various real-world applications, benefiting from the proliferation of mobile devices. However, the volume of time series data may vary sig…
CC-Time: Cross-Model and Cross-Modality Time Series Forecasting
Peng Chen, Yihang Wang, Yang Shu +6
With the success of pre-trained language models (PLMs) in various application fields beyond natural language processing, language models have raised emerging attention in the field…
MUSE: MCTS-Driven Red Teaming Framework for Enhanced Multi-Turn Dialogue Safety in Large Language Models
Siyu Yan, Long Zeng, Xuecheng Wu +6
As large language models~(LLMs) become widely adopted, ensuring their alignment with human values is crucial to prevent jailbreaks where adversaries manipulate models to produce ha…
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…
DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables
Xiangfei Qiu, Yuhan Zhu, Zhengyu Li +3
Time series forecasting is essential in various domains. Compared to relying solely on endogenous variables (i.e., target variables), considering exogenous variables (i.e., covaria…
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
Xiangfei Qiu, Zhe Li, Wanghui Qiu +10
Time series anomaly detection (TSAD) plays an important role in many domains such as finance, transportation, and healthcare. With the ongoing instrumentation of reality, more time…
LightGTS: A Lightweight General Time Series Forecasting Model
Yihang Wang, Yuying Qiu, Peng Chen +5
Existing works on general time series forecasting build foundation models with heavy model parameters through large-scale multi-source pre-training. These models achieve superior g…
VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
Xingjian Wu, Xiangfei Qiu, Hongfan Gao +3
Probabilistic Time Series Forecasting (PTSF) plays a crucial role in decision-making across various fields, including economics, energy, and transportation. Most existing methods e…
Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
Xvyuan Liu, Xiangfei Qiu, Xingjian Wu +4
The forecasting of irregular multivariate time series (IMTS) is crucial in key areas such as healthcare, biomechanics, climate science, and astronomy. However, achieving accurate a…
AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification
Yuxuan Chen, Shanshan Huang, Yunyao Cheng +6
Time series classification (TSC) is an important task in time series analysis. Existing TSC methods mainly train on each single domain separately, suffering from a degradation in a…
Data Driven Decision Making with Time Series and Spatio-temporal Data
Bin Yang, Yuxuan Liang, Chenjuan Guo +1
Time series data captures properties that change over time. Such data occurs widely, ranging from the scientific and medical domains to the industrial and environmental domains. Wh…
Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperation
Sicong Liu, Yang Shu, Chenjuan Guo +1
Learning cooperative multi-agent policy from offline multi-task data that can generalize to unseen tasks with varying numbers of agents and targets is an attractive problem in many…
RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems
Biao Ouyang, Yingying Zhang, Hanyin Cheng +6
With the continued migration of storage to cloud database systems,the impact of slow queries in such systems on services and user experience is increasing. Root-cause diagnosis pla…