In this work, we propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder (BVAE) equipped with diffusion, denoise, and disentanglement, namely D3VAE.
Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables.
Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning.
Thus, Wind Power Forecasting (WPF) has been widely recognized as one of the most critical issues in wind power integration and operation.
Second, a user-specific travel intention is formulated as an aggregation combining home-town preference and generic travel intention together, where the generic travel intention is regarded as a mixture of inherent intentions that can be learned by Neural Topic Model (NTM).
Intuitively, the potential crowd flow of the new coming site can be implied by exploring the nearby sites.