1 citations · 2 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Cross-Modal Alignment Dataset
Chengsen Wang, Qi Qi, Zhongwen Rao +3
Conventional forecasting methods are trained end-to-end on unimodal time series, which limits their ability to exploit textual information and undermines their generalization in da…
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…
Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systems
Jindong Tian, Yuxuan Liang, Ronghui Xu +6
Air pollution significantly threatens human health and ecosystems, necessitating effective air quality prediction to inform public policy. Traditional approaches are generally cate…
Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer
Yihang Wang, Yuying Qiu, Peng Chen +6
With the growing availability of multi-domain time series data, there is an increasing demand for general forecasting models pre-trained on multi-source datasets to support diverse…
Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders
Qichao Shentu, Beibu Li, Kai Zhao +5
Time series anomaly detection plays a vital role in a wide range of applications. Existing methods require training one specific model for each dataset, which exhibits limited gene…
Enhancing Multivariate Time Series Forecasting with Mutual Information-driven Cross-Variable and Temporal Modeling
Shiyi Qi, Liangjian Wen, Yiduo Li +5
Recent advancements have underscored the impact of deep learning techniques on multivariate time series forecasting (MTSF). Generally, these techniques are bifurcated into two cate…