Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: In this submission, the authors propose to use coupled diffusion model with denoising mechanism to augment the time series. The forecasting can be performed directly with by the generative model. In particular, the diffusion model aims to deal to aleatoric uncertainty. Also, the authors propose to perform latent variable disentangling to enhance interpretation. 
strengths_and_weaknesses: Originality
This method is built on several original insights, especially on the connection between noise and diffusion model. On the other hand, the model components each as BVAE and denoising disentangling are extended from previous works. 

Quality:
1.	The conductance of this study is of high quality in general. The experiments are solid with several ablations.
2.	However, one missing part in the discussion of mythological advance (not difference) with previous generative or diffusion model. For example, TimeGrad also relies on the diffusion model. Why this method outperforms a lot in the experiments.
3.	The overall presentation is satisfactory. However, it would be more reader-friendly for the audience to follow if there is a global picture in the main text. Also, several examples can be moved from the appendix to the main text.

Clarity：
1.	The motivation of the method is clearly addressed. The author emphasizes the noise challenges in short time forecasting setting.
2.	As the uncertainty itself is analyzed by separating aleatoric and epistemic uncertainty, the authors state that their proposed method can reduce the former. However, the reviewer finds it hard to follow the derivation of this conclusion (i.e., Lemma 1-2). It seems no bounds of this uncertainty are given.
3.	The connection between the diffusion model and disentanglement is missing? Why are these two components combined in one work?

Significance
This paper tackles an important issue and highlights a feasible augmentation strategy for noisy input.

questions: 1. What is the actual relationship between the aleatoric uncertainty and the diffusion model?
2. What is the advance of the diffusion model in this work wrt the application to time series?
2. What is the connection between the diffusion model and disentanglement?
limitations: The authors have mentioned several limitations in the main text. I agree with the authors that the process is sensitive to hyperparameters such as beta and time steps. One missing part is the computation performance, which is a common concern when using diffusion model.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The author proposed D3VAE, a coupled diffusion probabilistic model for time series forecasting problems. A diffusion process is introduced on top of the BVAE (Bidirectional Variational Auto-Encoder) to mitigate the effect of the random noise. Total correlation is used to disentangle the different latent variables Z to better extract dependencies from the input data hence improving the overall performance. Last, a denoising score matching (DSM) module is used to improve the accuracy of the generated results. Experimental results show that D3VAE perform better than other baseline models in most of the synthetic and real-world datasets.
strengths_and_weaknesses: Pros:
1. The design of this model is quite ingenious. The introduction of the DRL (disentangled representation learning) could cause noises in the reconstruction; therefore, a denoising module is used for better prediction accuracy. 
2. An extensive amount of experimental results are provided regarding the effectiveness of each module over a large scale of synthetic and real-world datasets. 
3. Experimental results show that D3VAE perform better than other baseline models in most of the synthetic and real-world dataset.


Cons:
1. There are not enough results for the disentanglement section. The author claim that using the TC can force the model to disentangle the latent variables {z1, z2, ..., zn}. However, there is not enough evaluation or results to prove that the learned latent variables are actually disentangled. The supplementary document only includes a graph that shows the difference between the ground truth and the prediction with different numbers of z used. That does not prove the claim that the author makes. The actual level of disentanglement for each zi or the single variables in zi are not investigated properly.
2. The overall structure and procedures of the proposed D3VAE are vague since there is only an illustration of the architecture of BVAE. It is hard for me to picture each module and its locations in the whole pipeline.
3. The claim of interpretability is not addressed in the paper. In section 2.4, the author states that the introduction of disentanglement can lead to better interpretability. However, there is not enough evidence regarding this topic. Although there are case studies in the supplementary document which might shed some insights, the relationship between those insights and the usage of disentangled representation learning is missing.
questions: 1. Which evidence can prove the learned latent variables Z are actually disentangled?
2. Which part of the results shows that the interpretability is improved compared to other baseline methods?
3. What is the actual architecture of D3VAE?
limitations: Improvement:
1. Add experiments for evaluating the disentanglement of the learned representations.
2. Add graphs to illustrate the increase in interpretability.
3. Refine the writing.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: The authors present a time-series forecasting model that can handle short examples. This is done using a generative model based on a BVAE. The experiments prove an excellent performance relative to SOTA techniques.
strengths_and_weaknesses: Strengths:
1. The presented method is developed using powerful theoretical ideas. This makes it easy to justify and understand the underlying reasons that explain the design.
2. The experiments show an important improvement relative to the specified benchmark.  
Weaknesses:
1. Fig. 2 shows important discontinuities in the estimated noises. These sudden breaks of continuity are not reflected or explained by the ground truth. Hence, there is a high probability that they are caused by numerical instabilities in the numerical algorithms.
2. There is no hyperparameter search, which strongly hints that there might be versions of the same pipeline capable of reaching even better performances.

questions: 1. How would the performance of the system change if the time series were shortened?
2. Why is the estimated noise discontinuous, when the ground truth is not.

limitations: I think this work is very useful for all those using time series forecasting for running their businesses. The beneficial impact that this technique could have on, for example, optimizing inventory levels, is very important.
review_title: null

Meta-review: metareview: I recommend to accept this paper. 

In this paper, the authors propose to address the time series forecasting problem with a generative modeling technique called D3VAE with both theoretically sounded insights and extensive empirical results. All the reviewers agree to accept this paper. I would encourage the authors to revise the paper based on the suggestions provided by reviewers. 
confidence: Certain
Decision: Accept