6 papers · 1 filter
Geolocation Representation from Large Language Models are Generic Enhancers for Spatio-Temporal Learning
Junlin He, Tong Nie, Wei Ma
In the geospatial domain, universal representation models are significantly less prevalent than their extensive use in natural language processing and computer vision. This discrep…
Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale
Tong Nie, Guoyang Qin, Lijun Sun +3
Spatiotemporal traffic data (STTD) displays complex correlational structures. Extensive advanced techniques have been designed to capture these structures for effective forecasting…
Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner
Tong Nie, Guoyang Qin, Wei Ma +1
Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional…
Channel-Aware Low-Rank Adaptation in Time Series Forecasting
Tong Nie, Yuewen Mei, Guoyang Qin +2
The balance between model capacity and generalization has been a key focus of recent discussions in long-term time series forecasting. Two representative channel strategies are clo…
Generalizable Implicit Neural Representation As a Universal Spatiotemporal Traffic Data Learner
Tong Nie, Guoyang Qin, Wei Ma +1
. Spatiotemporal Traffic Data (STTD) meas…
ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation
Tong Nie, Guoyang Qin, Wei Ma +2
Missing data is a pervasive issue in both scientific and engineering tasks, especially for the modeling of spatiotemporal data. This problem attracts many studies to contribute to…