Paper: PrACTiS: Perceiver-Attentional Copulas for Time Series (ICLR-2024)

Reviewer comment (Novelty):
The paper heaviliy extends upon TACTiS. They basically change the encoder. Thus novelty is low.

Author response:
Thank you for providing your feedback. We appreciate your perspective, and while we respect your comments, we would like to express our disagreement. Our model is designed with the explicit goal of enhancing TACTiS and optimizing it for multimodal scenarios. Through the incorporation of the Perceiver architecture as the encoder, we successfully encode multimodal data into a latent space domain. This improvement results in a performance boost of up to 40%, achieved with only half the computational resources of TACTiS. Additionally, we introduce midpoint inference to further reduce complexity, contributing to an overall enhancement in performance.

Reviewer comment (Experiments):
I would like to see more experiments, with at least al TACTiS paper datasets.

Author response:
In the course of our research, we conducted a total of six experiments, comprising three multimodal and three unimodal scenarios. It's worth noting that the unimodal datasets used in our experiments are sourced from the TACTiS paper. We firmly believe that our contributions in the realm of multimodal scenarios, coupled with the substantial improvement over the state-of-the-art, make our work well-suited for presentation at this conference.

Reviewer comment (Experiments):
The authors claim that the proposed model “can effectively handle synchronized, non-synchronized, and multimodal data, expanding its applicability to diverse domains”. Does any of the experiments demonstrate the model’s performance on handling non-synchronized or incomplete datasets?

Author response:
Regrettably, the unavailability of unsynchronized datasets has hindered our experimentation in this domain, a limitation shared with the TACTiS paper. Despite these challenges, we have managed to excel in short-term multimodal prediction scenarios by outperforming the TACTiS approach significantly. Some experiments proved to be particularly demanding, given our reliance on a short-term window to predict future short-term windows.

Reviewer comment (Experiments):
If none of the selected dataset contains non-synchronized timeseries, have the authors considered using more advanced architecture for benchmarking? E.g. AutoFormer; FedFormer; Informer; PatchTST.

Author response:
Thank you for recommending those insightful papers. Our approach is specifically tailored for short-term prediction, with a focus on enhancing the adaptability of the TACTiS model for multimodal scenarios. In contrast, AutoFormer, FedFormer, Informer, and PatchTST primarily address long-term prediction challenges. However, we acknowledge the importance of providing a comprehensive comparison, and in our revised version, we will include an analysis of these approaches in the context of medium-sized or short-term prediction. This will help underscore the distinctiveness of our methodology.