154 citations · 183 across the 12 of their papers we have counts for
11 papers · 1 filter
A2TTA: Anchored-and-Agile Test-Time Adaptation for Evolving Traffic Sensor Networks
Du Yin, Xiachong Lin, Yue Tan +4
Traffic forecasting is important for efficient traffic management and route planning in smart cities. Existing traffic forecasting studies typically assume fixed sensor graphs, ove…
AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting
Tengfei Lyu, Weijia Zhang, Jinliang Deng +1
Spatio-temporal forecasting is a critical component of various smart city applications, such as transportation optimization, energy management, and socio-economic analysis. Recentl…
Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
Du Yin, Jinliang Deng, Shuang Ao +6
Training models on spatio-temporal (ST) data poses an open problem due to the complicated and diverse nature of the data itself, and it is challenging to ensure the model's perform…
Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting
Zheng Dong, Renhe Jiang, Haotian Gao +4
Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications. While significant progress has been made in this area, fully capturing and lever…
Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting
Jinliang Deng, Feiyang Ye, Du Yin +3
Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis, characterized by extensive input sequences, as opposed to the shorter spans typical…
Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
Haotian Gao, Renhe Jiang, Zheng Dong +3
Spatiotemporal forecasting techniques are significant for various domains such as transportation, energy, and weather. Accurate prediction of spatiotemporal series remains challeng…
STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
Hangchen Liu, Zheng Dong, Renhe Jiang +4
With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing th…
Learning Gaussian Mixture Representations for Tensor Time Series Forecasting
Jiewen Deng, Jinliang Deng, Renhe Jiang +1
Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems…
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
Jinliang Deng, Xiusi Chen, Renhe Jiang +4
Multivariate time-series (MTS) forecasting is a paramount and fundamental problem in many real-world applications. The core issue in MTS forecasting is how to effectively model com…
A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting
Jinliang Deng, Xiusi Chen, Renhe Jiang +2
Multi-variate time series (MTS) data is a ubiquitous class of data abstraction in the real world. Any instance of MTS is generated from a hybrid dynamical system and their specific…
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
Renhe Jiang, Du Yin, Zhaonan Wang +7
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car…