Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting

Jinliang Deng1; Xiusi Chen2; Renhe Jiang3; Du Yin4; Yi Yang5; Xuan Song6; Ivor W. Tsang7

2024

10.1109/TKDE.2024.3371931

IEEE Transactions on Knowledge and Data Engineering   影响因子和分区

2326-3865

Deep learning disentanglement spatial-temporal data mining time series forecasting

SCI ; EI

其他

ENGINEERING

IEEE

1.Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, Australia
2.University of California, Los Angeles, USA
3.Center for Spatial Information Science, University of Tokyo, Tokyo, Japan
4.Department of Computer Science and Engineering, University of New South Wales, Sydney, Australia
5.Tencent WXG, Guangzhou, China
6.SUSTech-UTokyo Joint Research Center on Super Smart City, Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China
7.Center for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore

Jinliang Deng,Xiusi Chen,Renhe Jiang,et al. Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting[J]. IEEE Transactions on Knowledge and Data Engineering,2024,PP(99):1-18.

Jinliang Deng.,Xiusi Chen.,Renhe Jiang.,Du Yin.,Yi Yang.,...&Ivor W. Tsang.(2024).Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting.IEEE Transactions on Knowledge and Data Engineering,PP(99),1-18.

Jinliang Deng,et al."Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting".IEEE Transactions on Knowledge and Data Engineering PP.99(2024):1-18.