Title: Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach
Venue: NeurIPS 2025
Accepted: yes
Keywords: time series analysis

time series generation

few shot generation

unified training
Abstract: Generative modeling of time series is a central challenge in time series analysis, particularly under data-scarce conditions. Despite recent advances in generative modeling, a comprehensive understanding of how state-of-the-art generative models perform under limited supervision remains lacking. In this work, we conduct the first large-scale study evaluating leading generative models in data-scarce settings, revealing a substantial performance gap between full-data and data-scarce regimes. To close this gap, we propose a unified diffusion-based generative framework that can synthesize high-fidelity time series across diverse domains using just a few examples. Our model is pretrained on a large, heterogeneous collection of time series datasets, enabling it to learn generalizable temporal representations. It further incorporates architectural innovations such as dynamic convolutional layers for flexible channel adaptation and dataset token conditioning for domain-aware generation. Without requiring abundant supervision, our unified model achieves state-of-the-art performance in few-shot settings—outperforming domain-specific baselines across a wide range of subset sizes. Remarkably, it also surpasses all baselines even when tested on full datasets benchmarks, highlighting the strength of pretraining and cross-domain generalization. We hope this work encourages the community to revisit few-shot generative modeling as a key problem in time series research and pursue unified solutions that scale efficiently across domains.  Code is available at https://github.com/azencot-group/ImagenFew.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a time series generative model based on ImagenTime. To accommodate flexible number of channels, the authors introduced dynamic convolution and learnable data token as signals for labeling data domain. The model demonstrated particular advantage in data scarcity settings. At the same time, it claims to be the first pretrained time series generative model.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\nThe modifications upon ImagenTime are intuitive. \nThe paper is easy to follow\n\nWeakness:\nThe ablation study on dataset token did not show the advantage of dataset token. The effect of dataset token is not convincing and hard to interpret. Does that mean as long as dataset token is used in pretraining, it doesn't matter if it is used during finetuning? And are the benchmark dataset all from the domains seen during the pretraining? If not, do the dataset that come from an unseen domain show a different behavior when you look at the effect of dataset token? The experiment mentioned in line 345-246 is not shown in table 4. This is probably not the right way to report results. \n\nI don't think Sine is a good dataset to include for evaluation, it might make more sense to be in the pretraining dataset. There is no distribution change for the sine dataset, so sine dataset does not pose any challenge in data-scarcity setting. The ETT datasets are also highly seasonal and probably not hard to learn in few shot settings. But stock dataset are hard but it was used in the pretraining dataset instead and there is no dataset from the financial domain in the evaluation. \n\n\n\nIt's probably not suitable to claim a systematic benchmark as a main contribution as there is only a few models compared. For example, TimeVAE which is more recent than TimeGAN is not compared. And data-scarcity setting might not be so overlooked as I remember there is such setting in the TimeVAE paper. \n\nIn figure 1A, it's only clear that your model performs better than baselines. But if your purpose it to show that the gap between #10 training samples and 100% training set for your model is smaller than baseline (drawn as x2.15), I don't think this goal is achieved. It's not obvious that the scaling behavior of your model is better than the orange and green line. You might reconsider what message should come cross.\n\nQUESTIONS:\nline 216: each dataset is assigned a unique token that acts as an identifier of its source domain. How is this token assigned? Is it randomly chosen? Is it the domain name converted to word embeddings? There is no information on this. \n\n\nThere is an inconsistency in the metrics reported. some experiments/analysis use three metrics: discriminative, predictive, and contextFID while some experiments/analysis only reports discriminative and contextFID. why is that?\n\nLIMITATIONS:\nThe authors only mentioned one line at the end of section 6.1 saying the results on a few datasets like weather and ECG are challenging. But nothing is discussed about addressing the mentioned part. It could be due to a lack of datasets from the same domain in the pretraining set. Some analysis on similarity of pretraining and few shot dataset may help you uncover the reason. Additionally, some visualization on latent representation of learned dataset token during finetuning overlayed with dataset token specified during pretraining could be interesting.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper is motivated by a key observation: even state-of-the-art generative models tend to suffer significant performance degradation in low-resource settings.\n\nTo address this challenge, the paper proposes a diffusion-based generative framework trained with a unified strategy, aiming to learn generalizable temporal representations that can bridge this performance gap. Specifically, it introduces two core components: (i) a dynamic convolution layer (DyConv) to handle channel mismatches across heterogeneous time series datasets during large-scale pretraining, and (ii) dataset label conditioning to guide the sampling process.\n\nThe paper underscores the importance of few-shot generative modeling in time series research and provides a step toward addressing this underexplored challenge.\n\nSTRENGTHS AND WEAKNESSES:\n## Quality\nThe paper presents a series of designs to adapt the ImagenTime model for large-scale unified generative pretraining. In particular, DyConv is proposed to address the issue of misaligned channels across multi-domain heterogeneous time series data, and a masking mechanism is introduced to handle variable-length time series converted into images. These design choices are generally reasonable.\n\nThe ablation study for DyConv is fair. The authors remove the DyConv module and replace it with static channel padding as a baseline. To ensure fair comparison, a masking mechanism is incorporated into the loss function so that the denoising network is only penalized on valid (unpadded) dimensions. This improves the credibility of the ablation results.\n\n## Clarity\nThe explanation of the loss function (Equation 1) is somewhat unclear, particularly regarding how the weight function $\\lambda(\\sigma)$ is defined.\n\nThe model introduces a dynamic binary mask applied to the padded input sequence, which is adjusted at runtime to distinguish between valid and padded time steps. While this mask design is reasonable for handling variable-length time series converted into images, the paper fails to explain how this dynamic adjustment is implemented.\n\nThe section on dataset label conditioning states that the label is mapped to a learnable embedding and injected into the denoising network via Adaptive Group Normalization (AdaGN) [17] during training and generation. However, the implementation details are vague—this part would benefit from a formula or schematic illustration.\n\n## Significance\nThe paper clearly highlights an important issue: although generative modeling is a promising approach to mitigating data scarcity, existing models are typically designed and evaluated under the assumption of abundant training data. This makes the paper relevant to the time series generation community.\n\n## Originality\nThe proposed framework builds upon ImagenTime, a 2024 time series generative model that transforms time series into image-shaped inputs to leverage diffusion models from the vision domain. While the paper justifies using ImagenTime as the backbone and introduces DyConv and dataset label conditioning for transferability, the overall novelty remains somewhat limited.\n\nQUESTIONS:\n## Questions\nBaseline selection: The paper compares against ImagenTime, DiffTS, KoVAE, and TimeGAN. While these baselines span a broad range of generative paradigms, some—like KoVAE and TimeGAN—are relatively classical. It's unclear whether these baselines represent current state-of-the-art performance.\n\nClarity issues remain unresolved, especially those mentioned earlier regarding model explanation.\n\nDataset label conditioning ablation: The paper states that models trained without dataset label guidance experience significant performance drops if not fine-tuned. However, it's unclear where this result is reported. If a corresponding table exists, please point to it explicitly.\n\nMetric averaging ambiguity: In Section 6.1 on few-shot benchmarks, Table 1 reports the average values of Discriminator Score (Disc.), Predictor Score (Pred.), and context-based FID (c-FID). However, it is unclear whether these averages refer to the mean across different domains or the mean of multiple runs for each metric. This ambiguity could lead to misinterpretation.\n\nTerminology inconsistencies: For instance, describing the model’s ability to process variable-length time series using dynamic masks as \"variable-length time series generation\" is somewhat imprecise. Similarly, describing the experiment in Section 6.2—on pretraining with different sequence lengths—as “cross-resolution transfer” may be inaccurate.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nProposes a new method for training unconditional time-series generation models by pretraining on heterogeneous time series data and finetuning on data from the target domain. For evaluation, it introduces a benchmark which evaluates different models under varying degrees of data scarcity.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n- The problem is clearly defined and thoroughly motivated\n- The model is compared to a wide range of pre-existing unconditional time-series generation models under varying degrees of data scarcity\n- The pretraining is also demonstrated to allow the model to better generalize across different sequence lengths\n- The ablation study evaluates the impact of two critical components of the approach\n- Makes valuable improvements to the ImagenTime architecture to accomodate multi-domain pretraining:\n\t- Dataset token\n\t- Dynamic convolution to handle different datasets having different number of dimensions per time step\n- Provides new insights into how to make the most out of the limited data available in unconditional time-series generation. Although pretraining techniques are common in other domains,they have not been thoroughly explored within the context of unconditional time-series generation. Ultimately, this might open up for generation of new domains in time-series which were previously inaccessible due to limited data.\n\n**Weaknesses:**\n\n- The paper focuses on relatively short sequence lengths, using sequence length 32 for the main results in Table 2, which limits real world use. This can be compared to the ImagenTime paper, which generates for up to 17k in their longest experiments.\n- Technical details that should be clarified:\n\t- What dataset is being compared to when using c-FID, discrimination score and predictive score in for the various scarcity levels? Is it the scarce variant? Or the complete dataset? Depending on the choice here, different conclusions might be drawn. If the scarce datasets are compared to the synthetic data, there is a risk that the model has learned to memorize the training data and produce copies. This would lead to very good scores, but the samples would just be copies of the original training data. There are evaluation measures that aim to measure the degree of originality of synthetic data, see e.g. [1].\n\t- The predictive score is not presented for model size ablation in Table 2\n\n[1] Alaa, A., Van Breugel, B., Saveliev, E.S., and van der Schaar, M.. (2022). How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:290-306 Available from https://proceedings.mlr.press/v162/alaa22a.html.\n\nQUESTIONS:\n1. What dataset is being used to compared with in Table 1? Is the synthetic data trained on 5% of the data compared to 100% of the data, or is it compared to the 5% subset?\n2. What do you think about the originality of the synthetic data itself? Is it copies of the training data, or something more? It could be worthwhile to evaluate this empirically (e.g. using [1]), especially if the answer to the previous questions is that the synthetic data trained on 5% is compared to the 5% subset.\n3. Why is predictive score not presented for the model size study in Table 2?\n4. Could you elaborate a bit on the similarities and differences between DynConv and DyLinear?\n5. How are the datasets divided into several sequences? E.g. the stocks dataset consists of a single sequence.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a unified generative modeling framework for time series generation that is pre-trained across a diverse set of datasets spanning multiple domains (e.g., finance, climate, energy, biomedical) and fine-tuned for specific target domains under low-data conditions. It uses a vision-based diffusion model that transforms time series into image representations, incorporates dynamic convolution (DyConv) layers to flexibly handle varying input/output channel sizes, and uses dataset-specific tokens to guide generation. The model achieves strong few-shot performance, supports variable sequence lengths, and shows robustness across model sizes and domains.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\nThe unified framework tackles a major limitation of prior generative models by enabling generalization across domains and strong performance in few-shot settings, outperforming state-of-the-art models like ImagenTime and DiffusionTS by large margins. Its use of delay-embedding image transformations leverages advances in vision diffusion models, while DyConv and dataset tokens offer architectural flexibility, efficiency, and improved fidelity across multivariate time series with varying lengths and channels. The comprehensive evaluation across datasets, sequence lengths, and model scales strengthens the validity of its claims.\n\nWeaknesses\nDespite the gains, the approach relies on image-based representations, which may introduce unnecessary inductive biases or miss native temporal dependencies compared to sequence-native architectures. The use of pretraining and fine-tuning still requires nontrivial computational resources and tuning. Additionally, while dataset tokens improve specificity, their utility diminishes without fine-tuning, and performance on some domains (e.g., Weather, ECG200) still lags in limited data settings, suggesting room for better domain adaptation or representation alignment.\n\nQUESTIONS:\n1. Why was delay embedding chosen over sequence-native encodings for diffusion modeling?\n\n2. What is the impact of the delay embedding parameters (e.g., window size) on generation quality?\n\n3. How robust is the dataset token mechanism when domain boundaries are ambiguous or overlapping?\n\n4. Does the image transformation degrade interpretability of the generated sequences?\n\n5. Could lightweight alternatives to DyConv (e.g., grouped or depthwise convolution) provide similar flexibility with lower cost?\n\n6. How sensitive is performance to the number and diversity of datasets used in pretraining?\n\n7. Are there cases where pretraining leads to negative transfer in fine-tuning?\n\nLIMITATIONS:\nThe authors acknowledge some dataset-specific limitations in performance (e.g., on Weather and ECG200), but a deeper analysis into why the model underperforms on these datasets is missing. Understanding whether the challenges stem from noise, irregular sampling, or lack of temporal structure could provide more actionable insights. Additionally, while the model spans high-stakes domains like finance and biomedical data, the discussion on potential societal risks—such as misuse of synthetic data, overreliance on generated samples, or downstream model degradation due to hallucinations—is minimal. The paper would benefit from explicitly addressing these concerns and proposing guardrails for responsible use.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}