Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: The authors handles multivariate forecasting problem by utilizing a univariate forecasting model to predict individual dimensions of multivariate setting. They validate the proposed model with its baseline (Transformer-based model, Informer, Autoformer) on benchmark datasets and the experimental results show that the proposed model outperforms its baselines on some tasks.

strength_and_weaknesses: The proposed method is technically simple, but it seems to show good performance, compared to popular time-series forecasting models on the  benchmark datasets. 

clarity,_quality,_novelty_and_reproducibility: I found the paper is clearly written and easy to understand.

summary_of_the_review: The authors provides a good description about their motivation and proposed method, if technically correct, seems to address the targeted problem. Although the shown results are quite interesting (regression tasks) with a good motivation, it is a bit hard to think the proposed method is novel as the proposed method is just a simple application of transformer-based univariate forecasting model on multivariate setting.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: The author propose to measure the quality of a simple univariate transformer on multi-variate problem without any specific agregative strategy. They show that this simple approach is empirically efficent on various tasks in different situations. The authors also try to measure the interaction between the channels during the forcasting process to understand the prediction. First, they simply quantify the impact of the number of channel by repeated random channel sampling experiments. Then they propose a gradient based method to quantify all single interactions.
Strong refererences are chosen to establish baseline performances on classical multivariate datasets and the comparison is interesting: multivariate models fail on data with low interactions between channels and succeed on Illness.
Then the authors propose to measure the data requirements of the different pipelines. Without surprise, the gap between simpler univariate approaches and more complex multivariate model increase when the dataset is reduced.
strength_and_weaknesses: **Strength**

Very simple and easy to understand approach... With nice performances

** Weaknesses**

The contribution is rather limited, the authors could have conducted more experiments

One of the remaining open question at the end of the reading is: are the different channels specific or generic in the considered multivariate dataset and, if they are specific, how does the univariate identify each of them ? [probably from the data history before the prediction].
The proposed interaction metric shows that the historical data are sufficient to predict the next step for 5 datasets out of 6... But we don't know if the historical data can be compensate by channel correlation.

legend (or caption details) is missing on Fig. 2
clarity,_quality,_novelty_and_reproducibility: Very clear, good reproducibility.

Weak novelty
summary_of_the_review: The message of the article is very clear and well illustrated but the contribution is rather limited. 

It is a smart but limited series of experiments that may interest the ICLR community.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper studied an interesting problem where the time series forecasting model may fail to benefit from the extra information in a multivariate setting. 
strength_and_weaknesses: Strengths
1. A novel problem is studied
2. The experiment design is comprehensive

Weaknesses:
1. Some model settings issues are not clearly explained in the paper. See questions below. 
clarity,_quality,_novelty_and_reproducibility: Overall the paper is written in good quality though several minor issues need to be addressed. The paper studied a novel finding and provided some interesting insights which help understand the model's behavior in forecasting multivariate time series. 
summary_of_the_review: This paper is a solid submission providing insights into pattern learning in multivariate time series forecasting. In particular, the authors design a series of experiments to explain the counterintuitive finding where the forecasting model performs better in a univariate setting. I have several questions listed below:

1. It's unclear why the authors don't use the traditional multivariate forecast setting with univariate models. Is it because of the computation cost?
2. How is one single univariate model trained on all the individual dimensions? Some details are needed here. 
3. What are the loss functions used to train the model? A shape-aware loss function (e.g., DILATE) or point-wise distance-based loss functions (e.g., MSE, MAE, etc.)? The latter type of function may cause the model to fail to capture the shape of the time series, which I think could be another potential reason why the univariate setting sometimes outperforms the multivariate setting.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This paper presents a simple method that allows univariate transformer-based time series forecasting models to be used for the multivariate setting. The efficacy of the proposed method is mainly demonstrated through experiments. The technical novelty of the proposed method is limited though. However, this might be acceptable if the paper could present a very in-depth analysis to justify why such a simple idea can work so effectively, making the paper scientifically more appealing to the ICLR community. This work may have potential for publication in the future if the paper could be improved along this direction.

justification_for_why_not_higher_score: The paper in its current form is below the acceptance threshold. Bring it above the threshold will require substantial revision after further research.

justification_for_why_not_lower_score: N/A
Decision: Reject