Title: A Diffusion Model for Regular Time Series Generation from Irregular Data with Completion and Masking
Venue: NeurIPS 2025
Accepted: yes
Keywords: Time Series Generation

Irregularly-sampled Time Series

Diffusion Models
Abstract: Generating realistic time series data is critical for applications in healthcare, finance, and climate science. However, irregular sampling and missing values present significant challenges. While prior methods address these irregularities, they often yield suboptimal results and incur high computational costs. Recent advances in regular time series generation, such as the diffusion-based ImagenTime model, demonstrate strong, fast, and scalable generative capabilities by transforming time series into image representations, making them a promising solution. However, extending ImagenTime to irregular sequences using simple masking introduces ``unnatural'' neighborhoods, where missing values replaced by zeros disrupt the learning process. To overcome this, we propose a novel two-step framework: first, a Time Series Transformer completes irregular sequences, creating natural neighborhoods; second, a vision-based diffusion model with masking minimizes dependence on the completed values. This hybrid approach leverages the strengths of both completion and masking, enabling robust and efficient generation of realistic time series. Our method achieves state-of-the-art performance across benchmarks, delivering a relative improvement in discriminative score by 70% and in computational cost by 85%.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper propose a novel two-step framework for generating realistic time series data. First, a time series transformer is employed to complete irregular sequences. Second, a vision-based diffusion model with masking minimizes dependence on the completed values. The proposed method achieves state-of-the-art performance, achieving significant improvement in discriminative score and computational cost . Overall, I feel this paper is well-written and of good quality. I have some concerns below, which I believe can be addressed. I am happy to raise my score if the authors approach them properly.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:** \n\n1. Overall, this paper is well-written and easy to follow.\n\n2. The paper conducted extensive experiments across 11 datasets and 12 tasks demonstrate state-of-the-art performance, with up to 70% improvement in discriminative score.\n\n3. The paper includes insightful ablation studies and toy examples to demonstrate the importance of natural neighborhoods.\n\n4. The main contribution of the proposed method is combining completion via a time series transformer with masking during training of a vision-based diffusion model, mitigating the issue of unnatural neighborhoods caused by zero-filled missing values.\n\n\n**Weaknesses:**\n\n1. The comparison is not thorough. The authors may include some VAE-Diffusion methods for comparison.\n\n2. The paper leans heavily on engineering contributions. It would benefit from a stronger theoretical foundation or more detailed justification for design choices. For example, why the proposed combination of TST and masking is particularly well-suited to the task compared to other alternatives.\n\nQUESTIONS:\nQ1: The discriminative score typically depends on the choice of classifier used to distinguish real from synthetic data. As shown in [1], only a strong classifier can effectively measure fidelity. Could the authors provide more details about the classifier used to compute this score?\n\nQ2: In Table 5, when the noise level increases from 0.1 to 0.15, the predictive performance exhibits significant degradation. This is somehow weird that the increase of noise is not that large but causes significant deterioration. It would be better to provide some explanination for this.\n\nQ3: Existing time series generation methods also include the combination of VAE and Diffusion. How is the method compared with these?\n\nQ4: Can the method be applied to time-series data with categorical and numerical attributes?\n\n\n\n\n\n\n\n\n\n**References:**\n\n[1] Tao, L., Xu, S., Wang, C.H., Suh, N. and Cheng, G., 2024. Discriminative estimation of total variation distance: A fidelity auditor for generative data. arXiv preprint arXiv:2405.15337.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a generative modeling method based on ImagenTime that can handle irregular time series data.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. Superior performance: performs well on multiple generation tasks and datasets, significantly outperforming existing methods\n\n2. High efficiency: Compared to traditional methods, it significantly reduces computational costs and has good practicality\n\nWeaknesses\n\n1. The main improvement is based on ImagenTime to adapt to irregular time series (specifically referring to missing values in normal time series). It feels that contribution is actually very marginal because most of it is from ImagenTime.\n\nQUESTIONS:\n1. There are two types of irregular data that I understand. The first is the data with missing values mentioned by the author, and the other is the data with irregular sampling frequency. Does the author's method seem to only address the first scenario?\n\n2. When applying the design of this paper to sequences without missing values, may it lead to performance degradation compared to the original ImagenTime?\n\n3. If I understand correctly, it seems that this paper mainly combines KoVAE's stage1 with ImagenTime, am I right?\n\n4.  I am confused about the experimental setup of this type of method. Generally speaking, the absence of time series in reality has a physical mechanism, such as the possibility of wind power sequences experiencing extreme weather leading to downtime, and so on. Does using random masking directly to simulate missing values really have practical value in the real physical world? In addition, the simulation of missing values has a pre-defined length? Becasuse many missing values occur continuously. If they are only missing at certain time steps, it will be easier to solve them through imputation.\n\n5. Can the author provide some relevant experiments to prove the advantages of the integrated setting to the two-stage setting that first performing imputation before generating tasks?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a two-step framework for generating regular time series from irregularly sampled data. The proposed approach first completes the irregular time series using a Time Series Transformer (TST), and then applies a vision-based diffusion model (ImagenTime) with masking to generate realistic sequences. The method is designed to balance between information completion and robustness to errors in imputation. Extensive empirical results are provided, demonstrating improved performance over prior work like KoVAE and GT-GAN.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- The idea of combining time series completion with masking in a diffusion-based generative framework is intuitive and practically useful.\n\n- The proposed approach shows strong empirical results across a wide range of datasets, sequence lengths, and missing rates.\n\n- The paper is well-organized and clearly written, with helpful illustrations (e.g., Fig. 1, Fig. 2) to convey the method and its motivation.\n\n**Weakness**\n\n- Lack of Novelty in Core Technique: While the proposed method achieves strong results, it largely combines two well-established components (TST for completion and ImagenTime for diffusion generation) with a fairly straightforward masking trick. The main idea of using TST to impute and then applying ImagenTime with masking is more of an engineering integration than a fundamentally new modeling contribution. There is no clear technical novelty in the diffusion formulation or architectural design.\n\n- Unclear Justification of Design Choices: The use of TST as the completion module is not well-justified beyond empirical comparison. Why is TST more appropriate than other imputation strategies, such as Gaussian processes, diffusion-based imputation (e.g., CSDI), or continuous-time models? The proposed \"natural neighborhood\" argument seems loosely motivated. While the visual inspection of kernel weights (Fig. 1) is illustrative, it lacks rigorous theoretical or quantitative support.\n\nQUESTIONS:\n- How does the proposed method compare to recent diffusion-based imputation methods like CSDI if used as a first step instead of TST?\n\n- Did the authors consider architectures where masking is applied within the diffusion backbone (e.g., sparse convolutions or attention masks) rather than only at the loss level?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 1, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel generative framework for producing regularly sampled time series from irregular and incomplete data. Irregular sampling is a common challenge in real-world applications like healthcare and finance. While previous methods such as GT-GAN and KoVAE attempted to address this, they suffer from high computational costs. To overcome these limitations, the authors propose a two-step approach. First, they use a Time Series Transformer (TST) to complete the irregular sequences, creating more natural temporal neighborhoods. This approach benefits from structured input after completion. Extensive experiments on multiple datasets and sequence lengths demonstrate that the proposed method significantly outperforms existing approaches in both accuracy and efficiency.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n 1. The paper addresses an interesting problem. While previous methods mainly focused on how to effectively process information after applying simple zero-padding, this paper takes a different perspective by focusing on how to perform the padding itself more appropriately.\n 2. It proposes a simple yet effective method.\n\n**Weaknesses**\n 1. The core contribution of this paper lies in its novel approach to handling irregularity. To better highlight this contribution, it would be necessary to evaluate performance across a wider range of tasks such as forecasting and classification, where irregularity is also a key challenge. Moreover, it would be helpful to apply the proposed strategy to existing models that handle irregularity using zero-padding, such as GRU-D or CSDI, and assess whether any performance improvement is observed. Since the proposed method is relatively simple and reuses many existing components, demonstrating its effectiveness only through the generation task makes the contribution somewhat weak.\n 2. It would be better if Figure 1 were replaced with an example more closely related to a real-world problem. The current toy example does not have a direct connection to irregularity, which may lead to confusion. Additionally, the paper should provide a more concrete explanation of why the kernel in Figure 1F is considered more consistent compared to Figure 1E.\n\nQUESTIONS:\nSee Weaknesses\n\nLIMITATIONS:\nSee Weaknesses", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}