3 citations · 8 across the 19 of their papers we have counts for
13 papers · 1 filter
CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting
Chengyi Pan, Huangqimei Zheng, Hao Xue
Although multivariate time series forecasting has broad application prospects, effectively incorporating exogenous variables remains challenging. Prevailing discrete-time architect…
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…
AsyTO: Asymmetric Temporal Operator for Parameter-Efficient Multivariate Time Series Forecasting
Xiachong Lin, Du Yin, Hao Xue +5
Multivariate time-series forecasting faces a structural dilemma: sharing one temporal predictor across variables is parameter-efficient but forces heterogeneous variables through a…
Multivariate Time Series Forecasting needs Cross Variable Loss
Kuiye Ding, Yifan Hu, Hanchen Wang +1
Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics. While existing studies mainly focus on cross-…
UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation
Du Yin, Hao Xue, Jinliang Deng +4
In time-series generation, existing approaches typically handcraft ortrain a separate model for each dataset, which hinders their scalability and fails to leverage shared temporal…
TS-Fault: Benchmarking Time Series Forecasters Against Structural Faults
Yuyang Zhao, Lian Xu, Hao Miao +2
Time series forecasting (TSF) underpins consequential decisions in energy, transportation, finance, and healthcare, yet TSF models are almost universally ranked by a single number…
QuAnTS: Question Answering on Time Series
Felix Divo, Maurice Kraus, Anh Q. Nguyen +5
Text offers intuitive access to information. This can, in particular, complement the density of numerical time series, thereby enabling improved interactions with time series model…
Double-Diffusion: Balancing Speed, Accuracy, and Uncertainty in Probabilistic Forecasting for Urban Sensor Networks
Hanlin Dong, Arian Prabowo, Hao Xue +5
Urban sensor networks need forecasts that are accurate, carry useful uncertainty, and refresh fast enough to act on as new readings arrive. These goals conflict: deterministic mode…
Embedding spatial context in urban traffic forecasting with contrastive pre-training
Matthew Low, Arian Prabowo, Hao Xue +1
Urban traffic forecasting is a commonly encountered problem, with wide-ranging applications in fields such as urban planning, civil engineering and transport. In this paper, we stu…
BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics
Arian Prabowo, Xiachong Lin, Imran Razzak +4
Buildings play a crucial role in human well-being, influencing occupant comfort, health, and safety. Additionally, they contribute significantly to global energy consumption, accou…
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…
XXLTraffic: Expanding and Extremely Long Traffic forecasting beyond test adaptation
Du Yin, Hao Xue, Arian Prabowo +2
Traffic forecasting is crucial for smart cities and intelligent transportation initiatives, where deep learning has made significant progress in modeling complex spatio-temporal pa…
A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings
Xiachong Lin, Arian Prabowo, Imran Razzak +4
The increasing demand for sustainable energy solutions has driven the integration of digitalized buildings into the power grid, leveraging Internet-of-Things (IoT) technologies to…