Generative Time Series Forecasting with Diffusion, January 2023#
2. Affiliation:#
Moloud Abdar (Department of Electrical and Computer Engineering,
University of Alberta, Canada)
3. Keywords:#
Time series forecasting, generative modeling, variational auto-encoder, diffusion,
denoise, disentanglement
4. Urls:#
Paper: https://arxiv.org/abs/2107.02279
Github code: https://github.com/PaddlePaddle/PaddleSpatial/tree/main/research/D3VAE
5. Summary:#
(1): The background of this article is time series forecasting, which is a widely explored task of great importance in many applications. However, it is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series.
(2): Past methods include autoregressive models, convolutional neural networks, and switching models. However, they either have limited generative ability or lack interpretability. The approach proposed in this article is well motivated by utilizing generative modeling with diffusion, denoise, and disentanglement.
(3): The proposed approach is a bidirectional variational auto-encoder (BVAE) equipped with diffusion, denoise, and disentanglement, namely D3VAE. A coupled diffusion probabilistic model is proposed to augment the time series data without increasing the aleatoric uncertainty and implement a more tractable inference process with BVAE. To ensure the generated series move toward the true target, multiscale denoising score matching is integrated into the diffusion process for time series forecasting. The latent variable is treated in a multivariate manner and disentangled on top of minimizing total correlation to enhance interpretability and stability of the prediction.
(4): The approach is evaluated on both synthetic and real-world datasets, and it outperforms competitive algorithms with remarkable margins. The performance supports their goal of addressing the time series forecasting problem with generative modeling.
6. Methods:#
The methodological idea of this article is to address the time series forecasting problem with generative modeling using diffusion, denoise, and disentanglement. The proposed approach includes the following steps:
(1): Formulate the generative time series forecasting problem by learning the representation Z that captures useful signals of X, and map the low dimensional X to the latent space with high expressiveness.
(2): Develop a bidirectional variational auto-encoder (BVAE) equipped with diffusion, denoise, and disentanglement, namely D3VAE, to implement a more tractable inference process with coupled diffusion probabilistic models.
(3): Integrate multiscale denoising score matching into the diffusion process for time series forecasting to ensure the generated series move toward the true target.
(4): Disentangle the latent variable on top of minimizing total correlation to enhance interpretability and stability of the prediction.
(5): Evaluate the approach on both synthetic and real-world datasets, and demonstrate that it outperforms competitive algorithms with remarkable margins.
7. Conclusion:#
(1): The significance of this piece of work is to address the time series forecasting problem with generative modeling by utilizing diffusion, denoise, and disentanglement techniques, which outperforms existing competitive algorithms with remarkable margins.
(2): In terms of innovation point, the proposed approach incorporates diffusion, denoise, and disentanglement into generative modeling for time series forecasting, which is a novel and effective solution. In terms of performance, the approach achieves state-of-the-art results on both synthetic and real-world datasets. However, the workload required for implementing the approach may be relatively high due to the use of multiple techniques and models.