3 citations · 3 across the 9 of their papers we have counts for
10 papers · 1 filter
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…
Empowering Time Series Analysis with Large-Scale Multimodal Pretraining
Peng Chen, Siyuan Wang, Shiyan Hu +7
While existing time series foundation models primarily rely on large-scale unimodal pretraining, they lack complementary modalities to enhance time series understanding. Building m…
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…
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,…
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…
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…