no code implementations • 11 Oct 2023 • Jiawen Zhang, Xumeng Wen, Shun Zheng, Jia Li, Jiang Bian
Deep time-series forecasting plays an integral role in numerous practical applications.
no code implementations • 11 Oct 2023 • Chenguo Lin, Xumeng Wen, Wei Cao, Congrui Huang, Jiang Bian, Stephen Lin, Zhirong Wu
In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and allow the model to scale to large datasets, e. g., millions of temporal sequences.
no code implementations • 11 Oct 2023 • Han Zhang, Xumeng Wen, Shun Zheng, Wei Xu, Jiang Bian
Despite considerable efforts in developing effective learning models for tabular data, current transferable tabular models remain in their infancy, limited by either the lack of support for direct instruction following in new tasks or the neglect of acquiring foundational knowledge and capabilities from diverse tabular datasets.