102 citations · 322 across the 78 of their papers we have counts for
16 papers · 1 filter
EasyTime: Time Series Forecasting Made Easy
Xiangfei Qiu, Xiuwen Li, Ruiyang Pang +11
Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researche…
Assessing Pre-Trained Models for Transfer Learning Through Distribution of Spectral Components
Tengxue Zhang, Yang Shu, Xinyang Chen +3
Pre-trained model assessment for transfer learning aims to identify the optimal candidate for the downstream tasks from a model hub, without the need of time-consuming fine-tuning.…
Fully Automated Correlated Time Series Forecasting in Minutes
Xinle Wu, Xingjian Wu, Dalin Zhang +4
Societal and industrial infrastructures and systems increasingly leverage sensors that emit correlated time series. Forecasting of future values of such time series based on record…
MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast
Shiyan Hu, Kai Zhao, Xiangfei Qiu +4
Many methods have been proposed for unsupervised time series anomaly detection. Despite some progress, research on predicting future anomalies is still relatively scarce. Predictin…
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…
Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning
Kai Zhao, Zhihao Zhuang, Chenjuan Guo +3
Time series anomaly prediction plays an essential role in many real-world scenarios, such as environmental prevention and prompt maintenance of cyber-physical systems. However, exi…
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching
Xingjian Wu, Xiangfei Qiu, Zhengyu Li +5
Anomaly detection in multivariate time series is challenging as heterogeneous subsequence anomalies may occur. Reconstruction-based methods, which focus on learning normal patterns…
TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting
Zhe Li, Xiangfei Qiu, Peng Chen +8
Time Series Forecasting (TSF) is key functionality in numerous fields, such as financial investment, weather services, and energy management. Although increasingly capable TSF meth…
Orca: Ocean Significant Wave Height Estimation with Spatio-temporally Aware Large Language Models
Zhe Li, Ronghui Xu, Jilin Hu +4
Significant wave height (SWH) is a vital metric in marine science, and accurate SWH estimation is crucial for various applications, e.g., marine energy development, fishery, early…
Efficient Stochastic Routing in Path-Centric Uncertain Road Networks -- Extended Version
Chenjuan Guo, Ronghui Xu, Bin Yang +4
The availability of massive vehicle trajectory data enables the modeling of road-network constrained movement as travel-cost distributions rather than just single-valued costs, the…
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…
A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data
Hao Miao, Yan Zhao, Chenjuan Guo +5
The widespread deployment of wireless and mobile devices results in a proliferation of spatio-temporal data that is used in applications, e.g., traffic prediction, human mobility m…
QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models -- Extended Version
David Campos, Bin Yang, Tung Kieu +3
We are witnessing an increasing availability of streaming data that may contain valuable information on the underlying processes. It is thus attractive to be able to deploy machine…
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Xiangfei Qiu, Jilin Hu, Lekui Zhou +8
Time series are generated in diverse domains such as economic, traffic, health, and energy, where forecasting of future values has numerous important applications. Not surprisingly…
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
Peng Chen, Yingying Zhang, Yunyao Cheng +5
Transformers for time series forecasting mainly model time series from limited or fixed scales, making it challenging to capture different characteristics spanning various scales.…