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Spatio-Temporal Multi-Task Learning via Tensor Decomposition.

Spatio-Temporal Multi-Task Learning via Tensor Decomposition.

Authors :
Xu, Jianpeng
Zhou, Jiayu
Tan, Pang-Ning
Liu, Xi
Luo, Lifeng
Source :
IEEE Transactions on Knowledge & Data Engineering; Jun2021, Vol. 33 Issue 6, p2764-2775, 12p
Publication Year :
2021

Abstract

Predictive modeling of large-scale spatio-temporal data is an important but challenging problem as it requires training models that can simultaneously predict the target variables of interest at multiple locations while preserving the spatial and temporal dependencies of the data. In this paper, we investigate the effectiveness of applying a multi-task learning approach based on supervised tensor decomposition to the spatio-temporal prediction problem. Our proposed framework, known as SMART, encodes the data as a third-order tensor and extracts a set of interpretable, spatial and temporal latent factors from the data. An ensemble of spatial and temporal prediction models are trained using the latent factors as their predictor variables. Outputs from the ensemble model are aggregated to make predictions on test instances. The framework also allows known patterns from the domain to be incorporated as constraints to guide the tensor decomposition and ensemble learning processes. As the data may grow over space and time, an incremental learning version of the framework is given to efficiently update the models. We perform extensive experiments using a global-scale climate dataset to evaluate the accuracy and efficiency of the models as well as interpretability of the latent factors. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10414347
Volume :
33
Issue :
6
Database :
Complementary Index
Journal :
IEEE Transactions on Knowledge & Data Engineering
Publication Type :
Academic Journal
Accession number :
150287535
Full Text :
https://doi.org/10.1109/TKDE.2019.2956713