A Diffusion Model for Regular Time Series Generation from Irregular Data with Completion and Masking
Authors: Gal Fadlon
Idan Arbiv
Nimrod Berman
Omri Azencot
Venue: NeurIPS 2025 poster ()
Keywords: Time Series Generation
Irregularly-sampled Time Series
Diffusion Models
Primary area: deep_learning
TL;DR: A novel framework for generating regular time series from irregularly-sampled data using a Time Series Transformer and vision diffusion model with masking, achieving state-of-the-art performance and efficiency.

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%.

A Diffusion Model for Regular Time Series Generation from Irregular Data with Completion and Masking
Gal Fadlon" Idan Arbiv' Nimrod Berman Omri Azencot
Department of Computer Science Ben- Curiin University of The Negev
{galfad, arbivid, bermann) Spoaaannn-ppoptteeeeaeee www.pppg.........eeooop ag. azencot@bgu.ae ac.il
Abstract
Generating realistic time series data is critical for applications in healthcare, finance, and science. However, irregular sampling and missing values present significant challenges. While prior methods address these irregularities, they often yield sub- optimal results and incur high computational costs. Recent advances in regular time series generation, such as the diffusion-based ImagenTTime model, demonstrate strong, fast, and scalable generative capabilities by transforming time series into image representations, making them promising solution. However, extending ImagenTime irregular sequences using simple masking introduces "unnatural" neighborhoods, where missing values replaced by zeros disrupt the learning pro- cess. To overcome this, we propose a novel two-step framework: first, a Time Series Transformer completes irregular sequences, creating natural neighborhoods; second, vision-based diffusion model with masking minimizes dependence on the completed values. This approach leverages the strengths of both completion and masking, enabling robust and efficient generation of realistic time series. Our method achieves -fee-of-tte-rrtt performance, achieving relative improvement in discriminative score by 70% and in computational cost by 85%. Code is https::/ http//ttttuu....c com cttp.b//ccc/eeccottooooooooooo/e azencot- group/ ImagenI2R
Introduction
Time series data is essential in fields such as healthcare, finance, and science, supporting critical tasks like forecasting trends, detecting anomalies, and analyzing patterns [18 24 34). Beyond direct analysis, generating synthetic time series has become increasingly valuable for creating realistic proxies of private data, testing systems under new scenarios, exploring "what-if" questions, and balancing datasets for training machine learning models (6 40 The ability generate realistic sequences enables deeper insights and robust applications across diverse domains.
In practice, however, time series is often irregular, with unevenly spaced measurements and missing values. These irregularities arise from limitations data collection processes, such as sensor failures, inconsistent sampling, interruptions in monitoring systems [28 44). This irregularity poses unique challenge for generating regular time series-where intervals are consistent, and the data follows the same distribution as if it were regularly observed (26). The main goal of this paper is to generate regular sequences by training on seggularly-samplle time series using deep neural networks.
The synthesis of regular time series from irregular ones is fundamental challenge, yet existing approaches remain scarce, with notable examples being GT-GAN and KoVAE (26 39). Unfortunately, these methods suffer from several limitations. First, they rely on generative adversarial networks (GANs) and variational autoencoders (VAEs), which have recently been surpassed in performance
"Equal Contribution
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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by diffusion- based tools [T] 59 38 demanding preprocessing step based on neural controlled differential equations (NCDEs) [28]. rendering these methods impractical for long time series. For instance, KoVAE requires s 6.5x
Second, both GT-GAN and KoVAE utilize computationally-
more training time comparison to our approach (See Fig. (3) Third, these methods inherently assume that the data, completed by NCDE, accurately reflects the true underlying distribution, which can introduce catastrophical errors when this assumption fails. In particular, their performance lags far behind that of models trained on regular time series, with ffae-of-thheat results irregular discriminative benchmarks being, on average, 540% worse than those on regular benchmarks.
To address these shortcomings, we base our approach on a recent diffusion model for time series, ImagenTime [38]. This method maps time series data to images, enabling the use of powerful vision-base based diffusion neural architectures. Leveraging a vision- based diffusion generator offers a significant advantage: regular time series can be generated from irregular ones using straightforward masking mechanism. Specifically, missing values in the series are seamlessly ignored during the denoising process in training, akin to techniques used in image inpainting tasks (37 12].
However, while this straightforward masking approach is simple and achieves strong results (see Tab. Z). we identify significant limitation. Missing values in the time series are mapped zeros in the image, resulting in "unnatural" neighborhoods that mix valid and invalid information. This can pose challenges for diffusion backbones, such as U-Nets with convolutional blocks. where the convolution kernels are not inherently masked and may inadvertently propagate errors from these artificial neighborhoods. To address this issue, we propose two-step generation process. In the first step, we complete the irregular series using our adaptation of an efficient Time Series Transformer (TST) approach [6]], significantly reducing computational overhead and enabling the generation of long time series. In the second step, we apply the straightforward masking approach described earlier. Crucially, this combination of completion and masking allows the model to learn from "natural" image neighborhoods while mitigating the reliance on fully accurate completed information through the use of masked loss minimization. Overall, our approach, built on a vision diffusion backbone, enables effective modeling of long time series while making minimal assumptions about pre-completed data, resulting in significantly efficient and improved generation performance.
We conduct comprehensive evaluation of our approach on standard irregular time series benchmarks, benchmarking against state-of-the-ar methods. Our model consistently demonstrates superior gen- erative performance, effectively bridging the gap between regular and irregular settings. Furthermore, we extend the evaluation to medium-, long- and datra-long--engeee sequence generation, assessing performance across 12 datasets and 12 tasks. The results highlight the robustness and efficiency of our method, achieving consistent improvements over existing approaches. Our key contributions are summarized as follows:
1. We introduce a novel generative model for pregularly-samplee time series, leveraging vision-based diffusion approaches efficiently and effectively handle sequences ranging from short to long lengths.
2. In contrast to existing methods that assume completed information is drawn from the data distribution, we treat it as a weak conditioning signal and directly optimize on the observed signal using a masking strategy.
3. Our approach achieves state-of-the-art performance across multiple generative tasks, de- livering an average improvement of 70% in discriminative benchmarks while reducing computational requirements by 85% relative to competing methods.
2 Related Work
Diffusion models 449 have recently demonstrated groundbreaking generative capabilities, surpassing
VAEs and GANs
29 17
primarily image generation [21 14 45). The immense success of
diffusion-basee approaches has spurred a wave of recent advancements, encompassing both theoretical
developments [50] 35 and practical applications [37 22
30
4 3
Generative modeling of time series is an emerging field, with pioneering approaches predominantly Recently, inspired by the success of diffusion models, there has been growing trend to adapt these techniques to various time series tasks 53 43
relying on GANs [58
33 31 and VAEs [3 39
including generative modeling [T] 59 16]. Notably, the ImagenTime approach (38 has achieved
oftteoffthe-an performance regular generative tasks for sequences of varying lengths, from short

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B
C
score = 0.71 score = 0.67 score 0.32 Figure 1: A data point (A) is mapped to an image with zeros and the coordinates in the center (B). Denoising the entire image yields inferior kernels (D) in comparison to masking (E). Constructing natural neighborhoods (C), yields consistent kernels and better scores (F).
very long, by transforming time series into images and leveraging vision-based diffusion backbones. Our work builds on Imagen ImagenTTmme, extending it to address the challenging setting of generating regular time series information from seeguuaaly-saapllee data.
Irregular time series modeling has been longstanding task. Modern machine learning methods have made significant strides by framing the problem through the lens of differential equations [46] Subsequent efforts have explored alternative architectures, including recurrent neural networks 47 and transformers [61 21. However, learning from irregular sequences has received comparatively less attention, with notable contributions such GT-GAN and KoVAE 26 39], both relying NCDE [28]. Despite their promise, NCDE-based methods are costly during preprocessing and training, limiting the applicability of GT-GAN and VoV in handling long time series. Further, replacing NCDE by efficient components such as TST 61,, yields suboptimal results (see Tab. 7
3 Background
Problem statement. We learn the underlying distribution time series data from irregularly sampled observations and generating regular time series from it. Specifically, given set of irregularly sampled sequences, our goal is to learn a model that approximates the true data distribution Paau (21r) and enables sampling of complete time series T1.7 from the learned distribution pe (21:2). Formally, we consider a dataset of irregularly sampled time series, represented as {=/ 1y=1, J==1, where each sequence consists of observations at non-uniform time steps 4 i, [4,, (2,,4,, .., (n] with 4 and ty < T. The challenge is to leverage these incomplete sequences to model the full distribution and generate realistic, regularly sampled time series that align with the data distribution.
Imagen Time employs the delay embedding transformation to map time series data into images, en- abling their processing with powerful vision-based diffusion models 38 Given an input multivariate regular time series Fi.7 RdxT with d features and length T, the delay embedding constructs an image Timg E Rddxwxl by placing z's values over the columns of Zimg per channel, where w, are user-defined parameters. During training, noise is added the image Timg at different timesteps, forming (4) The diffusion model, parameterized by sg. learns to denoise these images by approx- imating the score function so (Zimg: l). Inference begins with noise sample Fing (7) N(,0) 7). This sample iteratively denoised using the learned score function to produce cleaned image Timg(()). The inverse delay embedding transform is then applied to Zimg (0). reconstructing the original time series T1.7. Importantly, the inverse transformation of delay embedding is inherently non unnuun, as be
the time series values repeated within the image representation, suggesting various designs considered as we discuss in Sec. Finally, crucial advantage of Imagen Time is its effectivity in handling long series, e.g., time series of length 65k: is transformed an image of size 256 x 256.
TST leverages the self-attention mechanism of Transformers to model temporal dependencies and long-range interactions in time-series data effectively. Unlike traditional sequence models such as RNNS or LSTMss TST processes the entire sequence simultaneously. enabling parallelism and mitigating the vanishing gradient problem. The architecture includes input projection to a higher- dimensional feature space, positional encodings to capture temporal order, and a stack of Transformer encoder layers with Hexxblle normalization and activation options. TST particularly suitable for imputation tasks, as it can handle pregularly-samplee data and missing values through explicit masking and preprocessing. Additionally, its self-attention mechanism inherently supports long sequences, making it robust for capturing global context and dependencies in extended time-series data. By eliminating the need for computationally expensive preprocessing techniques, such as calculating coefficients for cubic splines or other interpolation methods, TST achieves significant speed advantages while providing scalable, efficient, and accurate solution for tasks like forecasting, classification, anomaly detection, and imputation.

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irregular signal
TST encoder
GRU decoder
regular signal
AE train
autoencoder
image
noisy img.
U-Net
denoised
Train
Inference
noise
Figure In the first step (top), we train TST-based autoencoder, which we use during the second step (middle), where vision diffusion model is trained with masking over non-active pixels. Inference (bottom) done similarly to Imagen Time.
Method
While ImagenTime does not address the challenge of irreulaary-sampler time series, a simple extension can enable it to generate eggularl-samplee time series by training on irregular data. The key idea involves employing a mask during the loss computation. This mask ensures that only "active" pixels- corresponding to observed time series values-are considered in the loss calculation, while "non-active" pixels, representing missing information, are effectively ignored. This approach enables effective learning from incomplete data while preserving the integrity of the observed information, offering two key advantages: (i) the mask is
making it compatible with any diffusion backbone, and (ii) the inference procedure of Imagen remains entirely unchanged.
4.1 Unnatural image neighborhoods
Unfortunately, the straightforward approach has a fundamental limitation: although non-active pixels are ignored during loss computation. they are still processed by the network. In practice, missing values are replaced with zeros, resulting in "unnatural" pixel neighborhoods. Specifically, while zeros may occasionally occur in non-zero segments of time series, their repeated presence highly unlikely, leading to inconsistencies. In other words, masking is not applied at the architecture level, potentially hindering the effective learning of neural components.
To demonstrate this phenomenon, we consider the following toy experiment. We generate 1000 two-dimensional points, drawn from multivariate Gaussian distribution with four centers (Fig. [A]. Given this distribution, we create an irregular dataset of 3 x 4 images, Sirregulars by taking a data point, setting all pixels zero except those at the center, corresponding the and y coordinates of the original point (Fig. [B). Then, we train two diffusion models to: (i) predict the score across the entire image, and (ii) predict only the two central pixels via masking.
We evaluate the models by comparing their score estimation loss only on the two central pixels, regardless of the training strategy. Our results indicate that masking does not improve score estimation, yielding scores of 0.71 vs. 0.67 for Setups (i) and (ii), respectively. In addition, we also inspect the convolution kernels by averaging across channels the L1 norm of each spatial position (Fig. [ID.E). As can be seen, the kernel in Setup (i) heavily attends to zero-valued pixels instead of focusing on the essential central pixels, suggesting that "unnatural" neighbors may be detrimental. In contrast, the masked kernel from Setup (ii) largely ignores non-relevant zeros and prioritizes the middle pixels.
4.2 Our approach
One possible solution to alleviate the phenomenon of unnatural neighborhoods is to implement masking at the kernel level, but this would require modifications tailored to each neural architecture, thereby restricting the approach's 's fiexibility and its straightforward applicability across different models. For instance, while our work employs a convolutional U-Net, recent transformer- based architectures have emerged as highly effective diffusion backbones 41 Accommodating such diversity in architectures would require a more generalized solution.
To construct more natural pixel neighborhoods while remaining architecture-agnosiic,, we take inspiration from the two-stage pipelines of GT-GAN and KoVAE (2.(3) Our method likewise uses two-step training scheme. First, we complete missing values in irregularly sampled time series using

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TST [61] to obtain regularly sampled sequence. Second, we transform the completed series into an image and apply denoising as in ImagenTime, with one key change: during the loss computation we mask the pixels that originated from completion (see App. B following the straightforward masking
strategy discussed earlier. In the toy experiment of Sec. 4.1 the completed neighborhood (Fig. (Fi
enables learning of consistent kernels (Fig. [ and improves score estimation to 0.32.
We also replicated the synthetic experiment on real-world Stocks dataset, using larger convolu- tional kernel and comparing the two ways to complete irregular neighborhoods: (i) zero filling and (ii) neiural filling. The Stocks results mirrored the synthetic study, reinforcing our hypothesis that "unnatural" neighbors (e.g.. zeros) are detrimental. Evaluated by score ecore-estimation loss on active pixels, simple masking delivered only a marginal gain (0.81 - 0.79). Visual inspection likewise matched the synthetic patterns, In contrast, our proposed approach, which avoids zero padding and learning from semantically valid neighborhoods, significantly improved performance, reducing the loss to 0.29 and yielding more consistent kernel behavior. See App F. for visuals.
This combination of completion + masking addresses the two primary challenges of irregular se- quences. Completion creates natural neighborhoods so convolutional kernels learn from values closer the true data distribution; masking prevents over-reliance on imputed values by excluding them from the loss, striking a balance between leveraging and mitigating incomplete information. Figure illustrates our pipeline: autoencoder pretraining (top), main training (middle), and inference (bottom). T and T- denote the delay-embedding transform and its inverse; the fire and snowflake icons indicate trainable and frozen modules, respectively.
Importantly, we identify limitations with T-1 that was proposed in 38 Specifically, in their approach, only the first pixel corresponding each time series value is used for reconstruction. Specifically, if z; is mapped to multiple image indices, the original method selects the first corre- sponding pixel in the image for reconstruction. We modify this inverse transformation by aggregating information from all corresponding image indices and computing the average of the associated pixels for each Tę. For given TTLL E IR4, both methods ensure that ʃ (f(((())) = x. See Sec. 5.6 for an ablation study of these two methods.
Results
5.1 Quality vs. Complexity
We first compare the discriminative score and training time of our method against KoVAE across different sequence lengths (24,90 96, 768).
0.3
Discriminative Score
Both models were evaluated under identical conditions, utilizing the same GPU and batch size to ensure a fair comparison. Training time was measured until each model converged to its best result. The discriminative score and training time were averaged over all missing rates and datasets for each sequence length and each method separately. As illustrated in Fig. 3 and Tab. D our approach achieves an average speedup of approximately 6.5x and an average improvement of about 3,4 x in the discriminative score compared to Ko VAE The results demon- strate that our method not only trains signifi- cantly faster but also generates data that more closely resembles the real distribution compared to VAEE Full results can be seen in App. E.7
0.2
(24)
Ours 196)
KoVAF (96)
Ours 1768)
KoVAE (768)
Time (hours)
Figure Discriminative score vs. training time for our approach and KoVAE across different lengths (24, 96, and 768). Lower discriminative scores and shorter training times are better.
5.2 Quantitative Evaluation
Our quantitative evaluations assess missing rate setups of 30%, 50%, and 70%. For example, in the 30% missing rate case, we randomly omit 30% of the data in each training sample. Additionally, we extend the standard benchmark, which typically considers a sequence length of 24, to include longer sequences 96, 768, and 10,920, providing more comprehensive evaluation across varying temporal scales. We utilize diverse set of datasets, extending beyond common benchmarks such as Sine, Stock, Energy, and MuJoCo to include additional real-world datasets: ETTh1, ETTh2, ETTml, ETTm2, Weather, Electricity, KDD-Cup, and Traffic. We compare against the popular TimeGAN
5

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approach 158], adapted to handle irregular data by incorporating the time difference between samples as input. In addition, we also consider the recent GT-GAN [26] and KoVAE 39]
We evaluate the performance of our model using the discriminative and predictive tasks suggested by [58]. In the discriminative task, measure the similarity between real and synthetic samples by training a classifier to distinguish between the two, reporting [0.5 acc), where acc is the accuracy of the classifier. For the predictive task, we adopt the "train on synthetic, test on real" protocol, where a predictor is trained on synthetic data and tested on real data. The performance is evaluated using the Mean Absolute Error (MAE). We also consider irregular time series metries: the Context-Fl score 12 which quantifies the similarity in distribution between synthetic and real data, and the correlation score [33]. which evaluates the feature-leve relationship between the two datasets. The Context-FID score is computed by encoding both synthetic and real sequences using Ve2 [60] and calculating the FID score on the representations. The correlation score measures the covariance of features between real and synthetic data, with a focus on assessing their alignment. Full details are provided in App. D33 For metrics, lower scores are better.
Tab, 2 details the benchmark results for a sequence length of 24. The values represent averages over the 30,, 50% and 70% missing rates, where the full results are provided in Tab. [8 In general, our approach presents dramatic improvements across all metrics with respect to the second-best approach (typically KoVAE). Following [39, we define the relative improvement error as extl = (e2 f1)/e2. where 22 is the second- best error and é1 is ours, In this metric, averaged across all datasets. our method improves by 74.2%, 15.0% % 78.5% , 62.1 % in the discriminative, predictive, context-FID. and correlation scores, respectively. We also compared our approach to KoVAE in the medium (96) and long (768) lengths on all datasets excluding MuloCo and electricity. The results appear in Tab.
showing the superiority of our method and its advantages to longer horizons, where we achieved mean relative improve- ment of 72.6 % 29.7 %, 92.1 %, 73.25% % in the discrimina- tive, predictive, context-FID, and correlation scores. See the Finally, for ultra-long sequences (10,920) on the KDD-Cup dataset, our model showed im- provements of 36.6% % in the discriminative score (see Tab, 4 showing its ability to generate realistic synthetic data even extreme sequence lengths.
Table 4: Discriminative results of ultra-long (10,920) sequences on KDD-Cup for different missing rates.
full results in Tabs. 19 20
Method
30%
50% %
70%
KoVAE
0.375
0.410
0.499
Ours
0.155
0.288
0.392
5.3 Qualitative Evaluation
We evaluate the similarity between the generated sequences and the real data using qualitative metrics. Specifically, we employ two visualization techniques [58]: (i) projecting the real and synthetic data into a two-dimensional space using t-SNE (55), and (ii) performing kernel density estimation to visualize the corresponding probability density functions (PDFs). Fig. Iillustrates these results for the 70% missing rate setting over various datasets and sequence lengths: Energy (24), Weather (96), and Stocks (768). The top row shows the two-dimensional point clouds of real (blue), KoVAE (green), and our data obtained via t-SNE, while the bottom row displays their respective PDFs. Overall, approach demonstrates strong alignment in both visualizations. In the t- t-SNE plots (top row), high degree of overlap between real and synthetic samples is observed. Similarly, in the PDF plots (bottom row), the trends and behaviors of the distributions closely aligned. For additional results, including the regular and irregular 30% and 50% settings, please refer to App. E.6
Table Train time (hours) for lengths (24, 96, 768), averaged over missing rates (30%, 50% 70%).
Model
ETTh1
ETTh2
ETTml
ETTm2
Weather
Electricity
Energy
Sine
Stock
Mujoco
GT-GAN
7.44
3.68
5.14
3.60
5.04
6.45
5.25
4.09
3.15
2.17
24
KoVAE
6.49
10.79
10.14
5.840
16.57
2.04
1.15
Ours
1.28
2.76
0.96
1.43
3,66
2.44
1.00
0.48
0.21
0.60
KoVAE
19.70
10.82
14.61
26.51
22.06
13.68
10.76
6.45
Ours
1.52
1.90
1.24
1.85
1.87
1.72
1,46
0.76
768
VAD
31.53
37.66
67.97
72.58
52.93
21.04
14.64
16.27
Ours
5.38
2.61
12.20
7.49
9.47
4.96
8.24
2.74

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Table 2: Averaged results over 30%, 50%, 70% missing rates for length 24. Lower values are better.
Model
ETThl
ETTh2
ETTml
ETTm2
Weather
Electricity
Energy
Sime
Stock
TimeeGAN-A
0.499
0.499
0.499
0.499
0.497
0.499
0.474
0.497
0.479
Disc
GT-GAN
0.471
0.369
0:412
0.366
0.481
0.427
0.325
0.338
0.249
KoVAR
0.197
0.081
0.050
0.067
0.332
0.498
0.323
0.043
0.118
Ours
0.037
0.009
0.012
0.011
0.057
0.384
0.080
0.010
0.008
TimeGAN-Ar
0.267
0,336
0.235
0.314
0.394
0.262
0.457
0,334
0.072
Pred.
GT-GAN
0.186
0.092
0.125
0.094
0.145
148
0.069
0.096
0.020
KoVAI
0.057
0.054
0.045
0.050
0.057
0.047
0.050
0.074
0.017
Ours
0.053
0,046
0.044
0,044
0.022
0.049
0.047
0.069
0.012
TimeGAN-Ar
3.140
3.199
3.419
3.218
2.378
23.39
6.507
2,780
2.668
FID
GAN
2.212
8.635
14.29
6.385
2.758
9.993
1.531
1.698
2.181
KoVAB
1,518
0.248
0.180
0.280
3,699
6.163
0.629
0.037
0.369
Ours
0.124
0.035
0.047
0,024
0,170
3,580
0.132
0.015
0,036
TimeGAN- Ar
3.743
1.051
2.350
0.579
1.200
13.24
3.765
2.424
1.399
GT-GAN
7.148
0,916
2.467
0.356
0.791
14.92
3.889
3.282
0.261
KoVAE
0.183
0.177
0.130
0.262
2,899
4.283
2.630
0.041
0.064
Ours
0.084
0.054
0,065
0.039
0.396
2.031
0.922
0.015
0.019
Table 3: Averaged results over 30%,
50%,
70%
missing rates for length 96 (top) and 768 (bottom).
Model
ETTh1
ETTh2
ETTml
ETTm2
Weather
Energy
Sine
Stock
FID Pred. Disc.
KoVAE
0.284
0.103
0.276
0.089
0.341
0.356
0.239
0.099
Length = 96
Ours
0.070
0,053
0,040
0.030
0.152
0.185
0,003
0,017
KoVAE
0.062
0.057
0.054
0.050
0.046
0.080
0.165
0.020
Ours
0.053
0.049
0.045
0.044
0.025
0.049
0.155
KoVAE
5.842
1.111
4,070
0.996
3.681
4.694
4.381
0,868
Ours
0.357
0.508
0,171
0.142
0.394
0.309
0.017
0.109
FID Pred. Disc. Corr.
KoVAE
0.224
0.362
0.175
0.435
2.609
4,810
0.222
0.089
Ours
0.114
0.151
0.092
0.094
0,890
1.161
0.016
KoVAE
0.238
0.201
0.236
0.196
0.428
0.384
0.350
0.284
Length 768
Ours
0.088
0.045
0,058
0,052
0.102
0.213
0.006
0,022
KoVAE
0.072
0.069
0.060
0.076
0,070
0.087
0.226
0.031
Ours
0.053
0.056
0.047
0.050
0.027
0.042
0.204
0.013
KoVAE
13.92
8.304
13.50
8.279
17.49
24.51
38.60
7.273
Ours
0.882
0.439
0.391
0.264
0.318
0.796
0.237
0.160
Corr.
KoVAE
0.333
0.606
0.404
0.738
3.252
8.752
0.379
0.046
Ours
0.103
0.106
0.122
0.128
0.458
1.040
0,006
0.026
5.4 Irregularly-sampled data under noise
Our work primarily addresses irregllarly sampled time series data. However, in real-world scenarios, such data often includes noise due to sensor limitations and inaccuracies. To further enhance quantitative evaluation framework, we tackle the generative challenges of learning from irregular time series in noisy environments. Specifically, we propose novel setup to evaluate the model's capability to recover the true underlying distribution from data corrupted by both irregular sampling and Gaussian noise. In this setup, we simulate 50% missing rate and inject additive Gaussian noise sampled from a normal distribution N/0, o)), where a corresponds to the specified noise level (c.g.. 0.1, 0.15, and 0.2). Importantly, this noise is added independently of the original data distribution or scale. The evaluation is conducted on sequences of length 24 across four datasets: Weather, Etthl, Stocks. and Energy. Following the discriminative and predictive evaluation protocols for
Table 5: Discriminative and predictive scores for 50% missing rate on Weather, ETTh1, Stock, and Energy datasets with injected noise levels (0.1, 0. 0.15, and 0.2).
Weather
ETThl
Stock
Energy
N/R
Model
Disc.
Pred.
Disc.
Pred.
Dise.
Pred.
Disc.
Pred.
KoVAE
0.426
0.056
0.225
0.073
0.235
0.016
0.434
0.067
0.1
Ours
0.061
0.052
0.024
0.034
0.007
0.012
0.065
0.047
VOE
0.488
0.092
0.377
0.077
0.341
0.092
0.493
0.093
0.15
Ours
0.416
0.029
0.407
0.059
0.282
0.023
0.467
0.053
KoVAE
0.491
0.096
0.440
0.084
0.352
0.121
0.496
0.123
0.2
Ours
0.485
0.035
0.456
0.062
0.340
0.027
0.457
0.057
7

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Energy
Weather
Stocks
A
B 10
C
5
10
ISNE
-5
-10
10
-10
10
-10
10
-10
D
10
F
Real
Real
Real
Ours
Ours
Ours
Density
4
KOV
5.0
KoVAE
5.0
KoVAE
2
2.5
2.5
0.0
0.0
0.2
0.5
Figure 4: 2D t-SNE embeddings (top) and probability density functions (bottom) for real data vs. synthetic data from our method and Ko VAE, under 70% missing rate. From left to right: Energy (length 24), Weather (length 96), and Stock (length 768) datasets.
Table 6: Discriminative and predictive scores for 40% random vs. 40% continuous missingness on the Weather and Energy datasets with sequence lengths of 24 and 96. Lower is better.
Weather
Energy
Metrie
Random
Continuous
Random
Continuous
Discriminative +
0.057
0.053
0.081
0.082
Length = 24
Predictive
0.021
0.025
0.047
0.043
Discriminative +
0.154
0.165
0.185
0.191
Length 96
Predictive +
0.025
0.023
0.048
0.050
50% missing rate described earlier, we compare our approach against the most recent state-of-the-at method, Ko VAE, Tab. presents the results. For each noise rate (N/R), we report the discriminative (Disc.) and predictive (Pred.) scores, where lower values indicate better performance. Our method consistently outperforms KoVAE, achieving significant improvements. Specifically, we observe an average relative improvement of 26.8 % in the discriminative score and 50.3 % in the predictive score across all datasets and noise levels.
5.5 Robustness to missingness patterns
While many time series exhibit randomly missing values, in practice, missing values can also occur in contiguous blocks due to sensor outages or communication delays. study this effect, we compare model performance under two types of 40% missingness: (i) randomly dropped values, and (ii) continuous missing blocks. This experiment is conducted on the Weather and Energy datasets using sequence lengths of 24 and 96. Discriminative and predictive scores reported in Tab. [6] The results show that our method maintains strong performance under both missingness types, with some cases slightly favoring random missingness and others slightly favoring block-missingness. Overall, these findings confirm that our approach is robust across a range of missingness patterns, effectively handling both sporadic and structured data gaps.
5.6 Ablation Studies
To better understand the contributions of each component in our proposed architecture, we conducted series of ablation studies. We explore each of the components our approach separately, and, additionally, we modify recent approaches to include our completion strategy. Specifically, we consider the following models: (i) KoVAE TST, where the NCDE module in KoVAE is replaced by TST, as in our approach. (ii) TimeAutoDiff [51] + TST (iii): TransFusion 48 + TST: The latter two baselines are diffusion-based models designed for regular time series but not specifically for irregular time series data. Therefore, we used the TST module to impute the missing values. (iv) Mask Only, where the TST autoencoder removed, and we only apply the masking mechanism. In this setup, missing values are imputed using unnatural neighbors by filling them with zeros. (v) Ours Without

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Table Discriminative scores of the ablation study with 30%, 50%, and 70% drop-rate on Energy and Stock datasets for sequence lengths of 24, 96, and 768.
30%
50%
70%
Model
Energy
Stock
Energy
Stock
Energy
Stock
Len. 24
KoVAE TST
0.399
0.109
0.407
0.064
0.408
0.037
DiffAutoDiff + TST
0.293
0.100
0.329
0.101
0.468
0.375
TransFusion + TST
0.201
0.050
0.279
0.058
0.423
0.065
Ours (Mask Only)
0.157
0.087
0.269
0.168
0.372
0.237
Ours (Without Mask)
0.158
0.025
0,307
0.045
0.444
0.013
Ours
0.048
0.007
0.065
0.007
0.128
0.007
KoVAE+ TST
0.240
0.185
0.254
0.221
0.417
0.193
Len. -96
TimeAutoDiff TST
0.299
0.105
0.336
0.104
0.461
0.398
TransFusion TST
0.305
0.083
0.335
0.098
0.442
0.116
Ours (Mask Only)
0.490
0.174
0.422
0.263
0,480
0.388
Ours (Without Mask)
0.402
0.033
0.476
0.072
0.491
0.082
Ours
0.130
0.011
0.153
0.018
0.272
0.021
Len. -668
KoVAE TST
0,380
0.2225
0.418
0.243
0.385
0.186
AutoADt + TST
0.299
0.104
0.334
0.101
0.466
0.487
TransFusion + TST
0.367
0.113
0.395
0.121
0.451
0.131
Ours (Mask Only)
0.437
0.249
0.349
0.450
0.435
0.491
Ours (Without Mask)
0.364
0.027
0.353
0.102
0.325
0.102
Ours
0.170
0.025
0.244
0.033
0.251
0.013
Masking, where we leverage TST to complete missing values, and training is performed without masking. (vi) Our approach.
We quantitatively evaluated each model under the same experimental conditions and show the results in Tab. To provide extensive analysis, our tests include several missing rates (30%, 50%, and 70%) using two datasets, Energy and Stock. Further, we measured the perfor- mance across different sequence lengths (24, 96, and 768). Our findings show that the combination of TST-based completion and masking yields superior performance compared to all other setups. Specifically, the Mask Only and Ours (Without Masking) setups showed significant limitations in capturing the true data distribution, while the replacement of NCDE with TST (KoVAE + TST) fell short in comparison to our proposed architecture. In particular, our results reveal that replacing the NCDE imputation component in KoVAE with the TST imputation mechanism is not the primary factor driving the significant improvements achieved by our method. Moreover, even when employing a powerful time series diffusion- model like TransFusion combined with TST-based imputation, performance significantly degrades, struggling to capture the true distribution of the regular data. Overall, these results highlight the critical role of masking during the diffusion process and the importance of leveraging completion as a guide rather than a direct substitute for the true distribution.
We also ablate the impact of different image transformations on model performance, evaluating four methods: vanilla folding (reshapes sequence into a fixed-sia sied-size matrix with paoddag)) Gramian Angular Field, basic delay embedding, and our proposed inverse delay embedding (see App. A Results in Tab. [aland and Tab. show that geometric approaches- vanilla folding and our enhanced Detbetter suit our method due to their structural clarity, where each pixel maps directly to a time point, facilitating mask usage and improving both discriminative and predictive performance. In contrast, GAF does not scale well to long sequences due to large image size. Our inverse transform also outperforms the original inverse of ImagenTime (38
We additionally conducted an extensive ablation study comparing a variety of completion strategies. These included simple methods such as, Gaussian noise (GN), zero-filling, linear interpolation (LI), and polynomial interpolation (PI); probabilistic techniques like stochastic imputation (SI) (sampling from a Gaussian distribution fitted to the non-NaN values in each slice); and more advanced learning- based approaches, including NCDE, CSDI [53] and our proposed Time Series Transformer (TST) completion. Our results in Tab. Sb show that when the neighbors are not natural-sut as in the case of zero completion or Gaussian noise completion-the model struggles more to generate data that closely follows the true distribution. In contrast, when using more natural completions (e.g.. polynomial, stochastic imputation, NCDE, CSDI, TST), the model consistently obtains very good results. This contirms that generating natural neighborhoods indeed enhances the generative quality without making the model completely reliant on the imputation quality.

---

Table 8: Comparative performance on Energy and Stock datasets for sequence length 24.
(a) Ablation study image transformation methods.
(h) Imputation methods with 50% drop-rate.
30%
50%
70%
Disc.
Pred.
Model
Energy
Stock
Energy
Stock
Energy
Stock
Model
Energy
Stock
Energy
Stock
Gramian Angular
0.291
0.011
0.313
0.157
0,363
0.183
GN
NoN
0.455
0.102
0.088
0,014
Doc.
Vanilla Folding
0.058
0.005
0,050
0.005
0.136
0.010
->
Basic DE
0.091
0.055
0.102
0.046
0.153
0.019
[
0.251
0.013
D.099
0.019
PI
0.201
0.012
0.053
0,016
Gramian Angular
0.049
0.033
0.048
0.055
0.049
0.016
Vanilla Folding
0.014
CSDI
0.088
0.012
D.088
0.013
SI Ours (TST)
0.069
0.010
0.047
0.013
Ours DE
0.047
0.012
0.047
0.012
0.047
0.011
Conclusions
In this work, introduced novel two-step framework for generating realistic regular time series from irregularly sampled sequences, By integrating a Time Series Transformer (TST) for completion with vision-based diffusion model leveraging masking, we effectively addressed the challenge of unnatural neighborhoods inherent in direct masking approaches. This hybrid strategy ensures that the diffusion model benefits from more structured and meaningful input while mitigating over-reliance on completed values. Our extensive evaluations across multiple benchmarks demonstrated state-of- the-art performance, with improvements of up 70% in discriminative score and an 85% reduction in computational cost over prior methods. Furthermore, our approach scales effectively to long time series, significantly outperforming existing generative models in both accuracy and efficiency.
Beyond these advancements, our work highlights the broader potential of integrating completion and masking strategies in generative modeling, particularly in domains where irregular sampling and missing values are prevalent. Future directions include extending our framework to multimodal time series generation, exploring self-supervised objectives for improved imputation, and integrating adaptive masking techniques that dynamically adjust completion reliance. By bridging the gap between irregular and regular time series generation, our method opens new possibilities for high- fidelity synthetic data generation in critical fields such as healthcare, finance, and climate science.
Acknowledgments
This research was partially supported by the Lynn and William Frankel Center of the Computer Science Department, Ben-Gurion University of the Negev, an ISF grant 668/21, an ISF equipment grant, and by the Israeli Council for Higher Education (CHE) via the Data Science Research Center, Ben-Gurion University of the Negev, Israel.
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References
[1] Allen. Short term spectral analysis, synthesis, and modification by discrete Fourier transform. IEEE transactions acoustics, speech, and signal processing, 25(3):235-2333 1977.
[2] J. B. Allen and L. R. Rabiner. A unified approach to short-time Fourier analysis and synthesis. Proceedings of the IEEE, (5:11)) 1564, 1977.
[3] N. Berman, O. Joglekar, E. Kosman, D. Di Castro, and O. Azencot. Towards general modality translation with contrastive and predictive latent diffusion bridge. In Advances in Neural Information Processing Systems (NeurIPS) 39, 2025.
[4] N. Berman, E. Kosman, D. D. Castro, and O. Azencot. Reviving life on the edge: Joint score-based graph generation of rich edge attributes. Trans. Mach. Learn. Res., 2025.
[5] N. Berman, I. Naiman, M. Eliasof, H. Zisling, and O. Azencot. One-step offline distillation of diffusion-based models via Koopman modeling. In Advances in Neural Information Processing Systems (NeurIPS) 39, 2025.
[6] E. Brophy, Z. Wang, Q. She, and T. Ward. Generative adversarial networks in time series: systematic literature review. ACM Computing Surveys, 55(10))1-31, 2023.
[7] E. Brophy, Z. Wang. and T. E. Ward. Quick and easy time series generation with established image-based GANs, arXiv preprint arXiv: 1902v.99020055 902.05624, 2019.
[8] L. M. Candanedo, V. Feldheim, and D. Deramaix. Data driven prediction models of energy of appliances in a low-energy house. Energy and buildings, 140:81-97, 2017.
[9] Y. Chen, K. Ren, Y. Wang. Y. Fang, W. Sun, and D. Li. ContiFormer: Continuous-ting transformer for irregular time series modeling. Advances in Neural Information Processing Systems, 37, 2023.
[10] Z. Chen, Wu, Y. Leng, J. Chen, Liu, Tan, Cui, K. Wang, He, S. Zhao, Bian, and D. P. Mandic. ResGrad: Residual denoising diffusion probabilistic models for text to speech. arXiv preprint 1rXiv:2212.145188 2022.
[11] A. Coletta, S. Gopalakrishnan, D. Borrajo. and S. Vyetrenko. On the constrained time-series
generation problem. Advances in Neural Information Processing Systems, 37, 2023.
[12] C. Corneanu, R. Gadde, and A. M. Martinez. LatentPaint: Image inpainting in latent space with diffusion models. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 4334-4343, 2024.
[13] A. Desai, C. Freeman, Z. Wang, and I. Beaver. Time A variational auto-encoder for multivariate time series generation. arXiv preprint arXiv:211 2021.
[14] P. Dhariwal and A. Nichol. Diffusion models beat GANs on image synthesis. Advances in neural information processing systems, 34:8780-8794, 2021.
[15] Flandrin, G. Rilling, and Goncalves. Empirical mode decomposition as a filter bank. IEEE signal processing letters, 11(2):122111) 2004.
[16] T. Gonen, I. Pemper, I. Naiman, N. Berman, and O. Azencot. Time series generation under data scarcity: unified generative modeling approach. In Advances Neural Information Processing Systems (NeurIPS) 39, 2025.
[17] J. Goodfellow. J. Pouget-Abbdie,, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems 27, pages 2672-2680, 2014.
[18] Z. Han, J. Zhao, H. Leung, K. Ma, and W. Wang. A review of deep learning models for time series prediction. IEEE Sensors Journal, 21(6):7833-788) -848, 2019.
[19] N. Hatami, Y. Gavet, and J. Debayle. Classification of time-series images using deep convo- lutional neural nerworks. Tenth international conference on machine vision (ICMV 2017), volume 10696, pages 242-249, SPIE, 2018.

---

[20] J. Hellermann and S. Lessmann. Leveraging image- based generative adversarial networks for time series generation, arXiv preprint arXiv:21/2.08060, 2021.
[21] J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840 11664006851,, 2020.
[22] Ho, I. Salimans, A. Gritsenko, W. Chan, M. Norouzi, and D. Fleet. Video diffusion models. Advances in Neural Information Processing Systems, 35:8633-8646, 2022.
[23] J. Hogue. Metro Interstate Traffic Volume. UCI Machine Learning Repository, 2019. DOI: http://bbborr/10024442//////.//
[24] H. Ismail Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P.-A. Muller. Deep learning for time series classification: a review. Data mining and knowledge discovery, 33(4)9117-963, 2019.
[25] Jeha, M. Schneide,, Mercado, S. Kapoor, R. S. Nirwan, V. Flunkert, J. Gasthaus, and T. Januschowski. PSA-GAN: Progressive self attention GANs for synthetic time series, In The Tenth International Conference on Learning Representations, 2022.
[26] Jeon, Kim, H. Song. S. Cho, and N. Park. GT-GAN: General purpose time series synthesis with generative adversarial networks. Advances in Neural Information Processing Systems, 35:36999-33 -3799, 2022.
[27] T. Karras, M. Aittala, T. Aila, and S. Laine. Elucidating the design space of diffusion-based generative models. Advances neural information processing systems, 35:26565-26577, 2022.
[28] Kidger, J. Morrill, J. Foster, and T. Lyons. Neural controlled differential equations for irregular time series. Advances in Neural Information Processing Systems, 33:6696-6707, 2020.
[29] D. P. Kingma and M. Welling. Auto-encoding variational Bayes. In 2nd International Confer- ence on Learning Representations, ICLR, 2014.
[30] F. Kreuk, G. Synnaeve, A. Polyak. U. Singer, A. Défossez, J. Copet, D. Parikh, Y. Taigman, and Y. Adi. AudioGen: Textually guided audio generation. In The Eleventh International Conference on Learning Representations, ICLR, 2023.
[31] X. Li, V. Metsis, H. Wang. and A. H. H. Ngu. TTS-GAN: A transformer-based time-series generative adversarial network. In International conference on artificial intelligence in medicine, pages 133-143. Springer, 2022.
[32] Z. Li, S. Li, and X. Yan. Time series images: Vision transformer for irregularly sampled time series. Advances in Neural Information Processing Systems, 36, 2023.
[33] S. Liao, H. Ni, L. Szpruch, M. Wiese, M. Sabate Sabate-Vidales, and B. Xiao. Conditional Sig- Wasserstein GANs for time series generation. arXiv preprint arXiv:2006. 05421, 2020.
[34] B. Lim and S. Zohren. Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A, 379(2194)22200009) 2021.
[35] Y. Lipman, R. T. Q Chen, H. Ben-Hamu, M. Nickel, and M. Le. Flow matching for generative modeling. In The Eleventh International Conference on Learning Representations, ICLR, 2023.
[36] H. Liu, 7. Chen, Y. Yuan, X. Mei, X. Liu, D. Mandic, W. Wang, and M. D. Plumbley. Au- dioLDM: Text-to-audio generation with latent diffusion models. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learn- ing Research, pages 11450-214744 PMLR, 23-29 Jul 2023.
[37] A. Lugmayr, M. Danelljan, A. Romero, F. Yu, R. Timofte, and L. Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1146-11471, 2022.
[38] I. Naiman, N. Berman, I. Pemper, I. Arbiv, G. Fadlon, and O. Azencot. Utilizing image transforms and diffusion models for generative modeling of short and long time series. Advances in Neural Information Processing Systems, 38, 2024.
12

---

[39] I. Naiman, N. B. Erichson, P. Ren, M. W. Mahoney, and O. Azencot. Generative modeling of regular and irregular time series data via Koopman VAEs. In The Twelfth International Conference on Learning Representations, ICLR, 2024.
[40] L. Nochumsohn and O. Azencot. Data augmentation policy search for long-term forecasting. Trans. Mach. Learn. Res., 2025.
[41] W. Peebles and S. Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4195-4205, 2023.
[42] V. Popoy, I. Vovk, V. Gogoryan, T. Sadekova, and M. Kudinov. Grad-TTS: A diffusion probabilistic model for text-to-speech. In International Conference on Machine Learning, pages 1599-8608. PMLR, 2021.
[43] K. Rasul, C. Seward, I. Schuster, and R. Vollgraf. Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In International Conference Machine Learning, pages 8857-8868. PMLR, 2021.
[44] P. Ren, R. Nakata, M. Lacour, I. Naiman, N. Nakata, J. Song. Z. Bi, O. A. Malik, D. Morozov, O. Azencot, et al. Learning physics for unveiling hidden earthquake ground motions via conditional generative modeling. arXiv preprint 24X.v:2407.15000 2024.
[45] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer. nigh-resolution image synthesis with latent diffussin models. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 10684-10695, 2022.
[46] Y. Rubanova, R. T. Chen, and D. K. Duvenaud. Latent ordinary differential equations for sregularly-sampled time series. Advances in neural information processing systems, 32, 2019.
[47] M. Schirmer, M. Eltayeb, S. Lessmann, and M. Rudolph. Modeling irregular time series with continuous recurrent units. In International conference on machine learning, pages 19388- 19405. PMLR, 2022.
[48] M. F. Sikder, R. Ramachandranpillai, and F. Heintz. Transfusion: Generating long, high fidelity time series using diffusion models with transformers. arXiv preprint 2.2 2023.
[49] Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pages 2256 226562225..26 PMLR, 2015.
[50] Y. Song, Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole. Score-based generative modeling through stochastic differential equations. In 9th International Conference on Learning Representations, ICLR, 2021.
[51] N. Suh, Y. Yang. D.-Y. Hsieh, Q. Luan, S. Xu, S. Zhu, and G. Cheng. TimeAutoDiff:: Combining autoencoder and diffusion model for time series tabular data synthesizing. arXiv preprint arXiv:2406. 16028, 2024.
[52] F. Takens. Detecting strange attractors in turbulence In Dynamical Systems and Turbulence, Warwick / 1980: proceedings of a symposium held at the University of Warwick 1979/80, pages 366-381. Springer, 2006.
[53] Y. Tashiro, J. Song, Y. Song, and S. Ermon. CSDI: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems, 34:24804-2488 2021.
[54] E. Todorov, T. Erez, and Y. Tassa. MuloCo: A physics engine for model- control. In 2012 JEEE/RSJ international conference on intelligent robots and systems, pages 5026-5033. IEEE, 2012.
[55] L. Van der Maaten and G. Hinton. Visualizing data using t-SNE. Journal of machine learning research, 9(1),, 2008
[56] M. Vetterli and C. Herley. Wavelets and filter banks: Theory and design. IEFE transactions on signal processing, (09))2207-2233) 1992.
13

---

[57] Z. Wang and T. Oates. Imaging time-series to improve classification and imputation. In Proceedings of the Twenty-Fourd International Joint Conference on Artificial Intelligence, IJCAI, pages 3939-3945. AAAI Press, 2015.
[58] J. Yoon, D. Jarrett, and M. Van der Schaar. Time-series generative adversarial networks. Advances neural information processing systems, 32, 2019.
[59] X. Yuan and Y. Qiao. Diffusion-TSy TS: Interpretable diffusion for general time series generation. In The Twelfth International Conference on Learning Representations, ICLR, 2024.
[60] Z. Yue, Y. Wang. J. Duan, Yang, C. Huang, Y. Tong, and B. Xu. TS2Vec: Towards universal representation of time series. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 8980-8987, 2022.
[61] G. Zerveas, S. Jayaraman, Patel, A. Bhamidipaty, and C. Fickhoff. transformer-based framework for multivariate time series representation learning. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pages 21 2114-2124, 2021.
[62] H. Zhou, S. Zhang, Peng. S. Zhang. J. Li, H. Xiong, and W. Zhang. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 11106-11115, 2021.
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NeurIPS Paper Checklist
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While "[Yes] " is generally preferable to "[No]" ", is perfectly acceptable to answer "[No] "provided proper justification is given (c.g., "error bars are not reported because it would be too computationally expensive" or "we were unable to find the license for the dataset we used"). "[No] or "[NA] is not grounds for rejection. While the questions are phrased in binary way, we acknowledge that the true answer often more nuanced, so please just use your best judgment and write justification to elaborate. All supporting evidence can appear either in the main paper or the supplemental material, provided in appendix. If you answer [Yes] to a question, in the justification please point to the section(s) where related material for the question can be found.
general, answering
IMPORTANT, please:
Delete this instruction block, but keep the section heading "NeurIPS Paper Checklist",
Keep the checklist subsection headings, questions/answers and guidelines below.
Do not modify the questions and only the provided macros for your answers.
1. Claims
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Question:
Answer: [Yes]
Justification: The abstract and introduction clearly outline the problem generating regular time series from irregular data and present the two-step method (completion + masking). which is consistently analyzed throughout the paper (see Abstract, Sec. Sec. and Sec.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results
be expected to generalize other settings.
is fine to include aspirational goals as motivation long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes]
15

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Justification:
The paper discusses the limitations of naive masking and inverse transforma-
tions in Sec. and includes ablation studies in Sec. 5.6 to analyze failure cases and model sensitivity.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
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The authors should reflect on the scope of the claims made, e.g., if the approach was only tested few datasets with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
Answer: [NA]
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Guidelines:
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A Time Series-to-Image Transformation
In this section, we provide brief discussion time series to image conversions. We then introduce Delay Embedding, the transformation we employed. Additionally, we describe an improvement we implemented to enhance the reversibility of delay embedding.
Time series to image conversion. The conversion of time series data into image representations has attracted significant interest for its ability to leverage computer vision methods in time series analysis. Techniques like Gramian Angular Fields [57). Recurrence Plots [19], and Line Graphs [2] allow for mapping time series data to visual formats, enabling tasks such as classification and imputation.
In the field of speech analysis, the short-time Fourier transform (STFT) [T 2 56 15) is crucial for capturing frequency variations over time, which is vital for processing audio and speech data. Recent advancements have also explored incorporating mel-spectrograms in diffusion models, particularly in connection with latent diffusion spaces [2 [0] 36]. Furthermore, generative approaches such as Wasserstein GANs
20 have been applied to time series in image form.
Vanilla Folding is a straightforward transformation. Given a time series x, we reshape into an image Fing by filling rows from left to right and moving to the next row upon reaching the end, padding with zeros if necessary. The inverse transformation reconstructs the original time series by reading the non non-padded region rowewise. Despite its simplicity, this method scales well to very long sequences. Folding can also be interpreted as a specific case of delay embedding, as we explain below.
Delay Embedding and Enhanced Reverse Transformation converts a univariate time series T1.L R into an image by structuring its information into columns and applying zero-padding as needed. This transformation is controlled by two hyperparameters: m, which defines the skip value, and n, which determines the number of columns. Given any channel of time series, the corresponding matrix X is formed as follows:
T1
Tm+1
FL-n
X
Rxxq
In+m+1
JL
where q= [[L - n)/ml, /m]. To match the input requirements a neural network, the resulting image Ting is padded with zeros. This transformation is applied independently to each channel, and for multivariate time series, the matrices X from different channels are concatenated along a new axis. Given imput signal e g the output is transformed representation Fing R xnxq, which then zero-padee prro-p to obtain Ting E R Xxxx, Delay embedding efficiently scales to long sequences; for instance, choosing m = n 256 enables the encoding of sequences up to 65k in length using 256 x 256 images.
Our primary innovation lies in the reverse transformation process. In the original approach, only the first pixel corresponding to each time series value is used for reconstruction. Specifically, if a;; is mapped to multiple image indices, the original method selects the first corresponding pixel in the image for reconstruction. In contrast, our approach aggregates information from all corresponding image indices by computing the average of the associated pixels for each x;. For a given T1.L E R both methods ensure that ff)))(
As shown in Table 9 our approach consistently outperforms the original approach across various drop rates. For example, at 30% drop rate, our delay embedding (DE) approach achieves a discriminative score of 0.020 for the ETTh dataset and 0.009 for ETTh2, compared to 0.023 and 0.018 with the naive approach, respectively. Similarly, at 50% drop rate, the new DE method reduces the discriminative score to 0.032 (ETThI) and 0.005 (ETTh2), outperforming the previous DE approach, which yielded scores of 0.040 and 0.037, respectively.
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Table 9: Discriminative scores on ETThl and ETTh2 for sequence length 24 compare the original inverse delay embedding in 30%, 50%, and 70% missing rates.
Delay Embedding
inverse, evaluated
5
30%
50% %
70%
Model
ETTh1
ETTh2
ETTh2
ETTh2
ETTh2
ETTh2
Ours + Old inverse
0.023
0.018
0.040
0.037
0.067
0.048
Ours + New inverse
0.020
0.009
0.032
0.005
0.058
0.013
B
Training Losses
Our model consists of two main components: an Autoencoder (AE) and a vision diffusion model (ImagenTime). For each component, we modified the loss to handle irregular data more effectively.
B.1 Autoencoder Training Loss
The TST-Based AE is trained to reconstruct only the known (non-missing) values of the input data. Since do not have access the regularly sampled time series during training, the model learns to infer missing values from the irregularly sampled data. The masked reconstruction loss is defined as:
Σ (1; xx)² IEO
O
where r' is the original input data, ă is the reconstructed output, O represents the set of observed (non-misss monn-missing) indices in the input æ, and |O] the number of observed values. This ensures that the loss function only penalizes reconstruction errors for the known values, without considering the missing ones.
B.2 Time Time Diffusion Training Loss
At the core of ImagenTime is the generative diffusion model, which follows the framework of Karras al. [27] for improved score-based modeling. The model employs second-order ODE for the reverse process, balancing fast sampling and high-quality generations.
For irregular time series data, we changed the training loss to ensure proper weighting of the diffusion steps and account for missing values. Given an input sequence x and a corresponding mask m indicating observed entries, the loss is defined as:
Can = Er,o [I[(D(# + on, x) mm]
(2)
where:
x is the input time series with missing values.
n ~ (0, 7) is standard Gaussian noise, scaled by m is the mask, indicating the observed values.
The model reconstructs the output using both observed and imputed indices, treating the imputed values as natural neighbors to the observed ones. However, the loss computed and compared only against the observed indices, ensuring that the comparison is made solely to the true distribution, unaffected by the imputed values. This allows the model to learn the distribution while maintaining accurate reconstruction of missing values.
C Inference Time Analysis
In this section, we present detailed comparison of the inference time between our approach and KoVAE, particularly in relation to sequence length. The evaluation is conducted across a range of sequence lengths: 24, 96, and 768. Additionally, we explore the relationship between inference time and sequence length for both models.
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C.1 Inference Time vs. Sequence Length
Figure iillustrates the relationship between inference time per sample (in seconds) and sequence length for both our model and KoVAE. While KoVAE demonstrates faster sampling on short se- quences, its efficiency degrades rapidly the sequence length increases. Ko KoVAE, which is based on sequential VAE architecture for time series, processes each time step individually throughout the entire sequence, causing its computational cost increase significantly with longer sequences and resulting in substantially longer inference times.
In contrast, our model maintains nearly constant inference time regardless of sequence length, making it highly efficient even for sequences as long as 5000 time steps. This is due to the use of delay embedding. which enables the model to compress long sequences into compact image representations with fixed dimensions. A clear turning point occurs at a sequence length of approximately 4500, beyond which our model consistently outperforms KoVAE in terms of inference time. This robust performance highlights the advantage of our method in terms of time efficiency, especially when dealing with long sequences.
C.2 Inference Time and Fidelity Comparison
As shown in Figure we evaluate the relationship between inference time and fidelity (indicated by the discriminative score) for single sequence. Lower discriminative scores correspond to higher fidelity model's predictions. On the other hand, inference time is measure of efficiency, with shorter times indicating greater computational efficiency.
Our approach consistently performs well by maintaining low discriminative score, which translates into higher fidelity in its predictions. This is achieved while also managing to keep the inference time relatively low, even for longer sequences. Notably, our model uses only 18 sampling steps regardless of sequence length, which results in stable and fast inference times across all configurations.
C.3 Performance Comparison
As observed from both figures, our model exhibits a favorable trade-off between inference efficiency and fidelity. While KoVAE might be more efficient for shorter sequences, performance degrades as sequence length increases, making it less suitable for long sequences. Our approach, however, remains consistently efficient and accurate, maintaining both low discriminative scores and linear inference times sequence lengths scale.
*
(24)
KoVAE
Inference Time per Sample
Discriminative Score
Ours (96)
KoVAE (96)
KoVAE (768)
5000
10000
15000
20000
Sequence Length
Time (Seconds)
Figure 5: Comparison inference time per sam- ple in seconds vs. sequence length of our model and Vae model.
Figure 6: Comparison of discriminative score vs. inference time of single sequence for our approach and Ko Ko across different sequence lengths (24, 96, and 768). Lower discriminative scores indicate higher fidelity, and shorter infer- ence times reflect greater efficiency.
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D Experimental Setup
D.1 Baseline Methods
We compare our method with several generative time series models designed for irregular data. KoVAE [39] is specifically designed handle irregularly sampled time series effectively. Additionally, we consider GT-GAN 226] another method tailored for irregular time series generation. Lastly, we evaluate against TimeGAN-At [58], re-designed version of the original TimeGAN. Since TimeGAN does not natively support irregular time series, we follow GT-GAN and compare them with their re-designed versions. Specifically, extending regular approaches support irregular TS requires the conversion of a dynamical module to its time-continuous version. we adapted it by converting its GRU layers to GRU-At, enabling the model exploit the time differences between observations and capture temporal dynamics.
D.2 Datasets
We conduct experiments using a combination of synthetic and real-world datasets, each designed to evaluate the model under various conditions, including regular and irregular time-series settings.
Sines This synthetic dataset contains features, where each feature is independently generated using sinusoidal functions with different frequencies and phases. Specifically, for each feature i E (1.... 5). the time-series data defined as x, (t) = sin(2 fit + 0.), where f; U[0, 1] and 0, U|- rr. The dataset is characterized by its continuity and periodic properties, making it suitable benchmark for evaluating the model's s ability to handle structured time-series data.
Stocks The Stocks dataset comprises daily historical Google stock price data from 2004 to 2019. It includes six features: high, low, opening, closing, adjusted closing prices, and trading volume. Unlike Sines, this dataset lacks periodicity and primarily exhibits random walk patterns. It is real-world dataset commonly used to benchmark financial time-series forecasting and modeling.
MuJoCo MuloCo (Multi-Joint dynamics with Contact) is versatile physics simulation framework
used generate multivariate time-series data [54]. The dataset contains 14 features representing state variables and control actions from simulated trajectories. This dataset is particularly suitable for evaluating models on dynamical systems and tasks involving physical interactions
Energy The Energy dataset is a real-world multivariate time-series dataset [8] derived from the UCI Appliance Energy Prediction dataset. It includes 28 features, which are correlated and exhibit noisy periodicity and continuous-valued measurements. This dataset provides a challenging benchmark for forecasting and modeling tasks involving environmental and appliance energy consumption data.
ETTh & ETTm The ETTh (Electricity Transformer Temperature - Hourly) and ETTm (Electricity Transformer Temperature Minute) datasets [62] capture electricity load data from two power stations with varying temporal resolutions. These datasets are used for short- and long-term time- series forecasting tasks and are part of an established benchmark for evaluating generative and predictive models.
Weather The Weather dataset includes daily meteorological measurements, such temperature, precipitation, snowfall, snow depth, and minimum and maximum temperatures, collected from the United States Historical Climatology Network (USHCN) The dataset comprises measurements from 1,218 weather stations and is used for analyzing climatic trends and weather forecasting tasks.
Electricity The Electricity dataset consists of electricity consumption data across multiple clients, represented as multivariate time-series. It is widely used for forecasting electricity loads and under- standing temporal consumption patterns in energy-related applications..
http:///aaaaaaaaatttoooooo/ootta NDP019
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Traffic The Traffic dataset 23 contains hourly traffic volume data for westbound 1-94 in the Minneapolis-St Paul, MN area, collected from 2012 2018. includes eight features, mixing numerical measurements (e.g., temperature, rainfall, snowfall, cloud coverage, traffic volume) with several categorical variables (e.g., holiday indicators, weather descriptions), making one of the few benchmarks that requires models to handle both continuous and categorical time-series data. The dataset captures multivariate, sequential patterns influenced by weather and holiday effects, making it particularly suitable generative benchmark for modeling complex dependencies across heterogeneous features.
D.3 Metries
Discriminative Score This metric measures the ability of model to differentiate between real and generated data. A lower discriminative score indicates that the generated data is more indistinguish- able from the real data, reflecting better generative performance. This score is typically computed by training a binary classifier and evaluating its accuracy in distinguishing between the two datasets. A score close to random guessing suggests that the synthetic data is nearly indistinguishable from real data.
The predictive score evaluates the quality of the generated data in terms of its utility for downstream predictive tasks. It is typically assessed using a supervised learning model trained on generated data and tested on real data, vice versa. A higher predictive score indicates better alignment between real and generated distributions.
Predictive Score
Context-FID Score A lower Frechet Inception Distance (FID) score indicates that synthetic sequences are more similar to the original data distribution. Paul et al. (2022) introduced a variation of FID, called Context-FID (Context-Frecdd in the original FID calculation with TS2Vec, a time series representation learning method [60]. Their findings suggest that models with the lowest Context-FID scores tend to achieve the best results in downstream tasks. Additionally, they demonstrated a strong correlation between the Context-FID score and the forecasting performance of generative models. To compute this score, synthetic and real time series samples are first generated, then encoded using a pre-trained TS2Vec model, after which the FID score is calculated based on the learned representations.
Inception Distance), which replaces the Inception model
Correlational Score Building on the approach from [33, we estimate the covariance between the tth and jth features of time series using the following formula:
T M t=1
(+Σ*)
T
Cov.J
x
T
x
1=1
To quantify the correlation between real and synthetic data, we compute the following metric:
cove.
cov..
10
cov?"' 2- cov
v Vcovy coN
D.4 Hyperparameters.
We summarize the key hyperparameters used our framework Tables 10 11 and corresponding to sequence lengths of 24, 96, and 768, respectively. The hyperparameters remain largely consistent across tasks, with variations in batch size, embedding dimensions, and image resolutions. We use the default EDM [27] sampler for all datasets and follow unified configuration for the U-Net architecture in the diffusion model. For further details, refer to 277. Additionally, all models were trained using the same learning rate schedule and optimization settings ensure comparability across different sequence lengths.
27

---

Table 10: Hyperparameter Settings for Sequence Length 24 Across Different Datasets
ETThi
ETTh2
ETTml
ETTm2
Weather
Electricity
Energy
Sine
Stock
Mujoco
General
luuge
16 16
16
8
leurning
butch size
128
128
128
128
128
128
125
128
125
teacher feurher.forring rate
0
0
0
0
0.2
0
DE:
emberidingte)
delayrm
Middle
40
40
40
n. Avade
5
5
6
5
num, Jayers
0
6
Diffusion
chounels
128
61
channets
2,
2,
2,
2,
2,
2,,
2,
2,,2,2
atteation resulution
[8. 2.
[8.4 [8,4.9]
[8. 4,
[8.4.9]
[8. 4.
[8,4.1]
[8, [8.4.2)
[8,4.
[8.4,2]
[8,4,
sompling ntps
18
18
18
18
18
Table 11: Hyperparameter Settings for Sequence Length 96 Across Different Datasets
Energy
Stock
General
16 16
16
16 10
16 16
32 32
32
16 16
16
learning rate
10
10
10"
10
batch size
32
64
128
128
32
128
128
16
teucher forcing
DE
unthedding(n)
16
16
16
16
32
32
16
16
delaytm)
TST
hidden Aim
40
40
40
40
40
40
40
40
heads
mum layerz
Diffasion
channels
128
128
channels
2.
[,
[,
[, 2.
[1 4]
[1,2,2.4 2,4
[.
[1
attention revoluttin
1,6844422 2,
[16, 8.4,
116,8,444
[16 4,
[32, 8,4,8,4,
[82,, 16,8,4,4) 4.
1,6,8.4,2
[16, 8.4,
xompling steps
18
18
Table 12: Hyperparameter Settings for Sequence Length 768 Across Different Datasets
ETT1
ETT22
ETTial
ETTm2
Weather
Energy
Sine
Stock
General
52.x
52: 32
52 32
32 32
52
32.
leaming
10-4
10-4
10-1
10-4
10-4
10--
10-1
10-1
hatch
32
32
16
52
tearher forcing
DE
12
42
32
32
24
24
Aldiion_di
40
40
40
Diflusion
128
128
128
11.2.2
11.22
[1.2.
[1.
[.
[1
[1.2.
18.5,
(39, 16.5.
16.8.4.2
[92,
[92,
16,8
sompling
18
18
28

---

11
score = 0.81 score = 0.79 score = 0.29
Figure Unnatural vs. natural neighborhoods stock data. data point (A) is mapped to an image with zeros and its coordinates centered (B). Denoising the entire image produces inferior kernels (D) compared masking (E). Constructing natural neighborhoods (C) yields consistent kernels and improved scores (F).
D.5 Natural vs. Unnatural Neighborhoods Experiment Setup
In this section, we provide a detailed explanation of the experimental setup introduced in Sec.
Experiment Setup. We first generate 1000 two-dimensional samples {p} drawn from a multivariate figure
Gaussian distribution, with means centered at (1, 1). (1. (1.. (-1, 1), and (-1.-1) illustrating all sampled data appears in Figure TTA. To better simulate our real world environment, we transform each 2D datapoint into 3 image by setting all pixels zero except those the center, which correspond to the x and coordinates of the original point (e.g., s(1, p(0] and s11,2] = p(1)). Figure D depicts an example of this transformation. We refer this dataset as Sirregular, as it simulates an "irregular" dataset containing zeros for missing values.
Next, construct second dataset, Sregular. in which the zero entries are replaced with linear or nonlinear transformations of p(0]. p[1], or both. This is intended to emulate a data-imputation step performed on the fist step of method, yielding more "natural" neighborhood of values. An data point example can be found in Figure [TC
We then compare three training setups:
Train a diffusion model to predict the score across the entire image (i.e., noise prediction in diffusion) on Sirregular.
2. Train a diffusion model to predict only the two central (coordinate) pixels, using our masking technique, on Siregular-
3. Train the same masked model in (2), but Sregular, where we have "natural neighbors."
All score models share simple architecture: Conv2D layer with a 3 x kernel, followed by a ReLU activation, and deconvolution layer that restores the original input size.
Evaluation. We employ two metrics:
1. Score Estimation Loss:
For fair comparison, we measure the score prediction error on the two central pixels (i.e., the original coordinates). regardless of the training strategy.
2. Kernel Analysis: We inspect the first-layer convolution kernels to determine which pixels the model focuses on. Since there are 64 output channels, compute the L₁ norm each spatial position across all channels and then average them.
E Additional Experiments and Analysis
E.1
"Unnatural" Neighborhoods: Stock Market Experiment
As described in the main paper, extend the synthetic experiment to real-world dataset of stock prices. Implementation details appear in the main text; here we present the visual results (moved to the appendix due to space constraints). See Fig.
E.2 Extending to Categorical and Numerical Features
We further extend our evaluation demonstrate that our method can handle mixed-type time series containing both numerical and categorical features. Categorical variables are mapped to learnable

---

Table 13: Discriminative scores on mixed-type data (Traffic) with 30%, 50%, and 70% drop-rate for sequence lengths of 24, and 96.
Model
30%
50%
70%
GT-GAN
0.481
0.473
0.485
Len.
KoVAE
0.154
0.172
0.222
Ours
0.061
0.064
0.087
GT-GAN
0.493
0.491
0.488
Len. 96
KoVAE
0.212
0.245
0.307
Ours
0.073
0.091
0.102
embedding vectors, which are jointly optimized with the generative model, transforming the input into a fully continuous representation that the diffusion process can operate on seamlessly. During generation, embeddings are mapped back discrete categories using simple postprocessing step. To validate this capability, we evaluated our method on the Traffic dataset which includes both numerical and categorical attributes, across multiple sequence lengths and missing rates. As reported in Tab. [3] our approach achieves the best discriminative scores compared to prior methods, demonstrating its ability to effectively capture temporal dependencies in mixed-typs time series while maintaining superior generative performance.
E.3
Computational Efficiency of Completion Strategies
In addition to performance, we also compare the computational efficiency of completion strategies. Both NCDE and CSDI rely costly operations- while CSDI involves up to 1000 sampling steps per imputation-whiieee for long sequences or gigh-dimensionll data. In contrast, TST is lightweight and scales efficiently while still achieving competitive or superior results. As shown Tab. 14 TST consistently trains faster and imputes orders of magnitude quicker than NCDE and CSDI; for instance, the Energy dataset with sequence length 768, NCDE could not be trained due to memory limits and CSDI required 84 hours of training and 2394 minutes for imputing 1024 samples, whereas TST completed training in just 6.67 hours and imputed all samples in only 6.26 seconds. These findings highlight TST's role scalable and efficient completion module, combining both high-quality generative performance and practical usability.
requires repeated cubic spline evaluations, makes them slow or infeasible
Table 14: Training (200 epochs) and imputation (1024 samples) times RTX 3090. TST is significantly faster than NCDE and CSDI, especially for long sequences; NCDE times are omitted where training infeasible.
Dataset
Seq. Len
TST (Ours)
NCDE
CSDI
Train (h)
Impute (s)
Train (h)
Impute (s)
Train (h)
Impute (min)
Energy
24
0.67
0.64
2.55
2.4
1.60
35.71
96
0.80
0.74
7.68
5,6
5.43
135.29
768
6.67
6.26
84.61
2394.71
24
0.10
0.19
0.67
2.0
0.18
15.40
Stock
96
0.13
0.20
2.45
7.2
0.40
46.78
768
1.10
1.20
59.32
56.0
3.55
514.08
E.4
Effect of Integrated Training Scheme
We investigate the impact of our integrated training scheme, which combines a short pre-training of the TST-based imputation module with joint training of both imputation and diffusion components. To assess advantages, we compare three settings: (i) joint training with brief TST pre- pre-training (our approach), (ii) fully joint training from scratch, and (iii) strict two-stage training where imputation is performed independently before generative training. Results in Tab. demmonntree that our integrated approach consistently achieves the best discriminative performance across the Energy and Stock datasets, while fully joint training without pre-training suffers from unstable reconstructions, and the two-stage setup underperforms due the lack of interaction between imputation and generative
30

---

Table 15: Average discriminative scores across 30%, 50%, and 70%
drop-rates on the Energy and
Stock datasets for sequence length of 768.
Model
Energy
Stock
Joint training without pre- traitiannng
0.278
0.076
Training independently
0.404
0.122
Joint training with pre-training (Ours)
0.222
0.024
learning. These findings highlight that even short pre-training phase can stabilize learning and enable the model effectively leverage imputed values during generation.
E.5 Quantitative Evaluation Full Results
We present the complete results, including standard deviations, for the experiment conducted in the main text in Sec. 5.2 In Tab. 18 Tab. T9 and Tab. 20] we provide detailed performance results across all missing rates of 30%, 50%, and 70% for sequence lengths of 24, 96, and 768, respectively.
E.6 Qualitative Evaluation Cont.
We provide the remaining missing rate analyses for the experiment described in Sec. 5.3 Fig. presents the analysis for a 50% missing rate, while Fig. 9 shows the analysis for 30% missing rate.
Additionally, quantitatively assess the overlap between the original and generated data cloud points the two-dimensional plane. We compute the Wasserstein distances between the original data and the generated samples. The results, presented in Table [6] indicate that our method consistently achieves the lowest Wasserstein distances across all missing rates and sequence lengths, outperforming VAE in every case. Notably, for long time series, demonstrating its robustness handling larger and more complex sequences with high missing rates. This underscores our approach's is ability to closely match the true data distribution, under challenging conditions, and its superior effectiveness managing incomplete time series.
model exhibits significantly better performance,
31

---

ETTh1
ETTm2
Sine
A 10
10
tSNE
-10
-10
-5
-10
0
-10
10
-5
5
D
E
F
Real
Real
10
Real
Ours
Ours
Density
KoVAE
KoVAE
KoVAE
2
2
0
0.2
0.4
0.6
0.8
0.2
0.4
0.6
0.8
0.0
0.5
1.0
Figure 8: 2D I-SNE embeddings (top) and probability density functions (bottom) for the 50% missing rate ETThl (short), ETTm2 (medium), and Sine (long) datasets.
ETTh1
ETTm2
Sine
A 10
ISNE
-10
-5
-10
-10
D
Ey.5
F
10
Density
KoV
5.0
KoVAE
KoVAE
4
2.5
0.0
0.2
0.4
0.6
0.8
0.2
0.4
0.6
0.8
0.0
0.5
1.0
Figure 9: 2D I-SNE embeddings (top) and probability density functions (bottom) for the 30% missing rate ETTh (short), ETTm2 (medium), and Sine (long) datasets.
Table 16: Wasserstein distances between original data clusters, generated samples, and clusters from KoVAE for various missing rates (30%, 50%, 70%) and sequence lengths. Lower values indicate better similarity.
Drop Energy Weather Stocks
Drop
70% Drop
Metric
Model
ETThl ETTm2
Sine
ETThl
ETTm2
Sine
KoVAF
3.97
7.64
5,53
4.78
5.92
6.25
3.38
4.37
12.07
Wass.l.
Ours
1.08
2.85
1.68
1.62
1.84
1.50
1.33
2.39
2.42
32

---

E.7
Complexity Analysis Cont.
Continuing from Sec. results.
5.1 where we summarized the upcoming experiment, we now present the full
In Table 17 report the ner training time (in hours) for method (Ours) and KoVAE until convergence, measured under identical hardware (RTX33090) and batch size settings. Convergence was defined by the best discriminative generated data every 10 training epochs and computed the discriminative score against the real data. Note that the times shown exclude any overhead for data generation evaluation; we only measure the pure training runtime until the point of highest discriminative performance.
achieved during training; specifically, we sampled the
These training times presented for three different sequence lengths (24, 96, 768) and three missing rates (30%, 50%, 70%). To assess the relative speedup of our approach over VAE, we averaged the training times of each model (when valid entries were available), computed the percentage speedup as
KoVAEE time Ours time
x 100%),
KoVAR time
and then averaged these speedups across the three missing rates. Our results show that:
At sequence length of 24, Ours converges approximately 80% faster average.
At sequence length of 96, Ours converges approximately 87% faster average.
At sequence length of 768, Ours converges approximately ~ 85% faster on average.
These figures underscore the substantial reduction in training time provided by our method, ranging from about 80% to 90% faster than Ko in most settings, while still achieving superior performance based on the discriminative score.
Table 17: Training Time (in hours) for irregular time series across sequence lengths (24, 96, 768) and missing rates (30%, 50%, 70%).
Seq. Len. Drop % Model ETTh1 ETTh2 ETTml ETTm2
Weather Energy
Sine
Stock
KoVAE Ours
5.00
15.08
11.65
7.49
9.23
12:20
5.78
2.71
30%
0.73
0.61
1.18
0.90
4.77
2,80
0.50
0.37
-7
KoVAF
4.98
4.73
7.80
6.68
2.20
28.59
6.86
1.03
50%
Ours
1.85
6.75
1.18
1.81
0.38
2.51
0.60
0.17
KoVAB
9.45
12.55
10.98
5.48
8.91
4.22
2.36
70%
Ours
1.27
0.92
0.52
1.58
5.82
2.02
0.35
0.08
KoVAE
25.80
8.10
13.79
25.92
33.87
4.94
6.90
7.71
30%
Ours
3.38
0.80
1.35
0.95
2.43
2.36
1.20
0.23
KoVAE
1.42
17.26
11.04
11.90
14.79
18.01
22.47
3.90
50%
Ours
0.59
0.57
1.32
0.95
1.06
1.33
0.47
0.46
KoVAE
31.91
7.12
19.90
15.77
17.52
18.1
2.91
7.76
70%
Ours
0.61
4.34
1.05
3.65
2.12
1.49
2.72
1.60
KoVAE
62.19
59.70
92.48
69.48
40.88
13.30
18.03
16.59
30%
Ours
4.72
1.96
7.57
6.45
16.46
3,50
2.93
3.11
768
KoVAE
15.52
37.41
26.30
45.66
46.99
19.86
5.49
14.84
50%
Ours
4.74
2.96
7.23
6.48
7.18
2.35
2.89
1.21
33

---

Table 18: Evaluation metrics for irregular time series with 24 sequence length (30%, 50%, 70% drop). Arrows (t / indicate whether higher or lower values are better.
Metric
Model
Fath
Fuh2
Ettll
Fitm2
Weather
Fllecticitty
Energy
Sine
Stock
Mujoco
30% Drop
TimeGAN
0.499
0.499
0.499
0.499
0.493
0.498
0.448
0.494
0.463
0.471
Disc.
GAN
0.473
0.371
0.420
0.369
0.472
0.409
0.333
0.363
0.251
0.249
KoYAE
0.208
0.075
0.045
0,077
0.229
0.497
0,280
0.035
0.162
0,123
Ours
0.020
0.009
0.014
0.006
0.029
0,399
0.048
0.013
0.007
0.009
TimeGAN
0,156
0.305
0.146
0,262
0.388
0,183
0.375
0.145
0.087
0.118
Pred.
GT-GAN
0.174
0.092
0.119
0.097
0.147
0.148
0.066
0.099
0,021
0.048
KoVAE
0,058
0.050
0.044
0.051
0.029
0.048
0.049
0.074
0.019
0.043
Ours
0.052
0.043
0.044
0.045
0.022
0.048
0.047
0.070
0.012
0.040
TimeGAN
2,934
2.565
2,437
2,924
1.612
18.04
4.440
2.919
2.475
3.628
Fid.
GT-GAN
1.689
15.26
27.43
6,902
1.161
9.907
1.305
1,810
2,429
0.656
KoVAE
1.769
0.211
0,181
0.609
0.539
7.606
0.645
0.048
0.741
0.428
Ours
0.071
0.023
0.023
0.010
0.018
3.451
0.033
0.032
0.009
0.028
TimeGAN
6.317
0.862
2,290
0.357
0.744
11.13
3,663
2.131
0.273
0.844
Corr. |
GT-GAN
7.167
0.918
2,519
0.358
0.782
14.93
3,855
3.141
0.264
0.803
KoVAE
0,148
0.088
0,162
0.483
1,852
4.351
2,910
0.049
0.032
0,561
Qurs
0.070
0.048
0.059
0.045
0.424
2.041
0.815
0.016
0.007
0.331
50% Drop
TimGANN
0.499
0.499
0.499
0.499
0.499
0.498
0.479
0.496
0.487
0.483
GT-GAN
0.462
0.371
0.407
0.376
0.496
0,391
0.317
0.372
0.265
0,270
KoVAE
0.188
0.077
0.498
0.499
0.298
0.030
0.092
0.117
Ours
0.032
0.005
0.013
0.013
0,035
0,360
0.065
0.014
0.007
0.007
TimeGAN
0,210
0.343
0.157
0.292
0.404
0.230
0,501
0.123
0.058
0.402
Pred.
GT-GAN
KoYAE
0,176
0.057
0.053
0.091
0.118
0.045
0.050
0.096
0.127
0.114
0,147
0.043
0.064
0.050
0.101
0.072
0.018
0.019
0,056
0.042
Ours
0.053
0.045
0.043
0.041
0.022
0.049
0.047
0.068
0.012
0.040
TimeGAN
4.131
3.132
2.642
2,693
2,839
20. 184
6,408
2.124
2.352
4.141
Fid.
GT-GAN
1.504
5.839
4,201
9.468
5,919
9.741
1.935
1.785
2.258
0.664
KoYAE
1.309
0.319
0.207
0.115
9,830
3.972
0.421
0.030
0.225
0.371
Ours
0.134
0.026
0.045
0.018
0,040
3.633
0.061
0.007
0.057
0.026
TimeGAN
2.293
0.932
1,864
0.352
0.835
13:79
3,761
2.192
2.021
0.825
Corn.
GT.
7.088
0.928
2.443
0.361
0.807
14.91
3.971
3.204
0.255
0.804
KoVAE
0.173
0.283
0.132
0.157
5.027
4.152
1.822
0.029
0.072
0.555
Ours
0.112
0,047
0.058
0.026
0.363
2.044
0.872
0.015
0.011
0.342
70% Drop
TimeGAN
0,500
0.499
0.500
0,500
0.499
0,500
0.496
0.500
0.488
0.494
Disc.
GT-GAN
0.478
0.366
0.409
0.353
0.475
0.480
0.325
0.278
0.230
0.275
KoVAE
0.196
0.081
0.048
0.046
0,269
0.498
0.392
0.065
0.101
0.1
Qurs
TimeGAN
0.436
0.359
0,401
0.387
0,390
0,372
0.496
0.734
0.072
0.442
Pred.
GT-GAN
0.207
0.084
0.137
0.089
0.162
0.149
0.076
0.088
0.020
0.051
KoYAE
0,056
0.060
0,046
0.048
0.028
0.051
0.052
0.076
0.012
0.044
Qurs
TimeGAN
2.356
3.900
5,179
4,036
2.683
31.95
8.674
3.246
3,178
4,432
Fid.
GT-GAN
3,442
4.805
11.24
2.784
1.194
10.33
1.354
1.498
1.857
0.671
KoYAE
1.477
0.215
0.153
0.116
0.727
7,610
0.820
0.033
0.141
0.292
Ours
0.167
0.056
0.073
0.043
3.653
0.301
0.007
0.041
0.044
TimeGAN
6.821
0.959
2,470
0.387
0.728
14.80
3,848
3.051
3.354
0.867
Corn.
GT-GAN
7.190
0.921
2.438
0.351
0.785
14.92
3,842
3.502
0.254
0.809
KoYAE
0.228
0.160
0.096
0.144
1,817
4.213
3.157
0.029
0.095
0.566
Ours
0.071
0.104
0.079
0.055
0.403
2.007
1.330
0.015
0.037
0.348
34

---

Table 19: Evaluation metrics for irregular time series with 96 sequence length (30%, 50%, 70% drop).
Arrows (t / indicate whether higher or lower values are better.
Metric
Model
Euhl
Buh2
Eitml
Bttm2
Weather
Energy
Sine
Stock
30%
Drop
KoVAE
0.255
0.096
0.264
0.075
0.290
0.416
0.244
0.114
Disc.
Ours
0.037
0.036
0.033
0.084
0.130
0.004
0.011
KoVAE
0.062
0.062
0.053
0.047
0.040
0.077
0.164
0.016
Pred. +
Ours
0.052
0.045
0.044
0.042
0.023
0.155
0,010
KoVAE
5.223
0.915
4.073
0.645
2.942
4.075
2.725
0,944
Fid J
Ours
0.156
0.112
0.147
0.169
0.018
0.110
KoVAE
0.201
0.301
0.177
2.730
0.058
0.086
Corr. |
Ours
0.102
0.075
0.087
0.042
0.306
0.827
0.017
0.016
50%
Drop
KoVAE
0.304
0.077
0.290
0.114
0.358
0.321
0.188
0.111
Dise. 1
Ours
0.070
0.067
0.047
0.017
0.190
0.153
0,003
0,018
KoVAE
0.063
0.055
0.053
0.054
0.051
0.063
0.161
0.016
Pred. 1
Ours
0.054
0.053
0.044
0.046
0.025
0.048
0.155
0,011
KoVAE
5,370
0.781
4,663
1.325
4.117
4.955
2.334
1.003
Fid J
Ours
0,516
0.487
0.179
0.130
0.567
0.182
0.018
0.105
KoVAE
0.246
0.288
0.247
0.638
2.490
5.142
0.044
0.085
Corr.
Ours
0.090
1.176
1.186
0.019
0,011
70%
Drop
KoVAE
0.294
0.137
0.274
0.079
0.374
0.332
0.286
0.073
Disc. +
0.057
0.182
0.002
KoVAE
0.062
0.055
0.056
0.049
0.047
0.064
0.171
0.029
Pred. J
Ours
0.053
0.050
0.046
0.044
0.027
0.051
0.155
0.012
KoVAF
6.932
1.638
3.473
1.019
5.051
8.083
0.657
Fid 4
Ours
0.399
0.926
0.187
0.235
0.447
0.553
0.016
0.112
KoVAE
0.226
0.496
0.101
0.465
2.607
4.611
0.565
0.095
Corr. J
Ours
0.087
0.159
0.100
0.126
1.187
1.470
0.013
0,015
35

---

Table 20: Evaluation metrics for irregular time series with 768 sequence length (30%, 50%, 70%
drop). Arrows (f / 1) indicate whether higher lower values are better.
Metric
Model
Euhl
Buhh
Bitml
Bttm2
Weather
Energy
Sine
Stock
30%
Drop
KoVAE
0.239
0.237
0.282
0.160
0.411
0.371
0.284
0.289
Disc.
Ours
0.045
0.032
0.067
0.047
0.093
0.145
0.005
0.025
KoVAE
0.077
0.074
0.059
0.081
0.061
0.089
0.223
0.020
Pred. +
Ours
0.052
0.056
0.047
0.027
0.025
0.204
0,011
KoVAE
11.16
9.448
11.47
8.869
12.21
25.50
38.71
7.431
Fid J
Ours
0.318
0.258
0.373
0.220
0.110
0.755
0.184
0.116
KoVAE
0.528
0,480
0.859
3.136
0.411
Corr. |
Ours
0.088
0.119
0.126
0.086
0.269
0.849
0.006
0.004
50% Drop
KoVAE
0,270
0.191
0.197
0.225
0.428
0.372
0.426
0.302
Dise. 1
Ours
0.061
0.030
0.061
0.048
0.097
0.244
0.009
0.029
KoVAE
0.074
0.064
0.056
0.084
0.064
0.086
0.222
0.023
Pred. 4
Ours
0.053
0.057
0.047
0.048
0.027
0.051
0.204
0.015
KoVAE
14.56
7.412
11.51
9,373
18.59
19.58
38.49
8.274
Fid J
Ours
0.589
0.248
0.305
0.211
0.225
0.916
0.211
0.199
KoVAE
0.290
0.574
0.428
0.842
4.836
12.63
0.336
0.085
Corr.
Ours
0.107
0,455
1.473
0.006
0,042
70%
Drop
KoVAE
0.206
0.176
0.231
0.203
0.445
0.409
0,340
0.263
Disc. +
0.072
0.005
KoVAE
0.065
0.071
0.065
0.062
0.086
0.088
0.234
0.051
Pred. J
Ours
0.054
0.056
0.047
0.052
0.028
0.050
0.204
0.013
KoVAF
16.05
8.052
17.54
6.595
21.69
28.46
38.60
6.114
Fid 4
Ours
1.739
0.812
0.496
0.362
0.619
0.718
0.317
0.167
KoVAE
0.331
0.718
0.305
0.515
1.786
5.49
0.391
0.013
Corr. J
Ours
0.118
0.071
0.133
0.179
0.650
0.798
0,006
0.034
36


Reviews:
[{"review_id": "VIuarrqpwm", "reviewer": "Reviewer_vxoC", "summary": "This 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.", "questions": "See Weaknesses", "limitations": "See Weaknesses", "rating": 2, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "**[A1]** As can be seen from the title and the contribution section, the central contribution of this paper lies in handling missing parts in irregular time series by generating natural observations and applying masked padding. My original question was why this key contribution—i.e., the proposed padding-like method—should be treated exclusively as a solution for generative modeling. Intuitively, such a method could also be applied to other tasks involving irregular time series (e.g., classification, regression). The authors claim that irregular time series are only used for imputation in such contexts; however, as demonstrated by [1], which has more than 700 citations, classification and regression tasks are also widely recognized as important in this field.\n\nIn both the main text and the authors’ rebuttal, I find no convincing explanation as to why the proposed method is specialized for generative modeling, nor why it could not address irregular time series problems in classification or regression. This omission diminishes the contribution of the paper. Of course, if the method itself had very high novelty, it could still be significant in its own right. However, the idea of leveraging an autoencoder to interpolate given time series is not new, as shown in [2]. Thus, I believe the method’s novelty is insufficient.\n\nThe authors argue that the method is “not padding,” but in the main text (line 145) they write:\n\n> “missing values are replaced with zeros, resulting in ‘unnatural’ pixel neighborhoods. Specifically, while zeros may occasionally occur in non-zero segments of a time series, their repeated presence is highly unlikely, leading to inconsistencies. In other words, masking is not applied at the architecture level, potentially hindering the effective learning of neural components.”\n\nSince the paper itself discusses the method in connection with padding, I believe the authors’ rebuttal claiming that it is “not padding” is not entirely appropriate.\n\n**[A2]** It is important for a paper to explain why the proposed method is capable of achieving good performance. In this work, I understood Figure 1 to be the main component responsible for providing such an explanation. However, the “score” (score estimation loss in score-based generative models) used in the paper is, in my opinion, insufficient to explain why the method performs well. The authors should provide an explanation of why a better estimation loss is obtained. This explanation need not be fully theoretical—an intuitive argument would suffice.\n\nMy original request was for a more intuitive explanation (e.g., visualization) of why Figure 1E is better than Figure 1D. However, the authors have again substituted such an explanation with the “score” metric. While much of deep learning research is indeed phenomenological, there should at least be an intuitive reason provided for strong performance. If that is not possible, richer and more diverse experiments should be conducted to validate the claims.\n\nThe time series domain has many cases where methods believed to work well ultimately failed to generalize. For example, when the DLinear model outperformed Transformer-based models, it was a great shock to the community. This is precisely why, in this field, it is particularly important to provide a clear explanation—and extensive empirical evidence—of why a proposed method works well.\n\nFor these reasons, I ultimately recommend rejection of this paper. That said, I respect the views of the authors, other reviewers, and the AC, and I hope my comments will help the AC in making their final decision.\n\n**References**  \n[1] Kidder et al., Neural Controlled Differential Equations for Irregular Time Series  \n[2] Jhin et al., Learnable Path in Neural Controlled Differential Equations  \n[3] Zeng et al., Are Transformers Effective for Time Series Forecasting?", "rebuttal": "## Response to Reviewer vxoC\n\nWe appreciate the reviewer's feedback and acknowledge the valuable points raised regarding our work. Below are our detailed responses to the comments and suggestions:\n\n---\n\n1. **“While previous methods mainly focused on how to effectively process information after applying simple zero-padding, ...”**\n\n    To clarify, previous methods (KoVAE, GT-GAN) did not use zero-padding; instead, they completed the missing values using NCDE, then treated the entire sequence, observed and imputed, as fully regular and trained VAE or GAN models on it. This strong assumption can harm performance, especially when the imputed values deviate from the true data distribution.\n\n---\n\n2. **“... it would be necessary to evaluate performance across a wider range of tasks such as forecasting and classification ... demonstrating its effectiveness only through the generation task makes the contribution somewhat weak.”**\n\n    We focused on generative modeling because it is the standard benchmark for irregular time-series completion (e.g., KoVAE, GT-GAN), and our contribution builds directly on this foundation. To ensure broader coverage and rigor, we significantly expanded prior benchmarks, from 4 to 11 datasets, and extended the evaluation beyond the standard sequence length of 24 to include lengths of 96, 768, and ultra-long time series. We also added a novel evaluation protocol with simulated Gaussian noise at multiple levels, enabling a more rigorous assessment of model robustness to noise and irregularity. We further enhanced the evaluation by incorporating both qualitative and quantitative analyses, and added additional metrics such as FID and correlation scores, offering a more complete assessment of generation quality.\n\n---\n\n3. **“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.”**\n\n    We conducted an extensive ablation study comparing various completion strategies to explore the impact of different imputation methods on generative quality. These methods include simple approaches such as Gaussian noise (GN), zero-filling, linear (LI) and polynomial interpolation (PI); and advanced learning-based approaches including GRU-D, NCDE, CSDI, and our proposed Time Series Transformer (TST) completion. As shown in Tab. 1 below (which extending Tab. 6 from the paper), when local neighborhoods are unnatural, such as with zero-filling, Gaussian noise or weak imputation methods like linear interpolation, the model struggles to generate samples that closely follow the true data distribution. Conversely, more natural completion methods (PI, GRU-D, NCDE, CSDI, TST) consistently yield strong generative performance, confirming the importance of generating natural neighborhoods without overly relying on imputation accuracy.\n\n    To further motivate our choice of TST as the completion strategy, we emphasize that methods like CSDI and NCDE depend on computationally expensive operations such as cubic spline calculation in NCDE or 1000 sampling steps, respectively, which becomes extremely slow or infeasible for long sequences or high-dimensional data due to high memory and runtime demands as can be shown in the lower table below. In conclusion, TST is a lightweight and scalable module that offers competitive or better performance with significantly improved efficiency. See Tab. 2 below for full experiment results.\n\n   **Table 1: Discriminative and predictive scores for different imputation methods on Energy and Stock datasets (sequence length 24, 50% missing rate)**\n\n   | Imputation Method             | Disc. Energy | Disc. Stock | Pred. Energy | Pred. Stock |\n   |------------------------------|--------------|-------------|--------------|-------------|\n   | Gaussian noise (→ NaN)       | 0.457        | 0.102       | 0.058        | 0.014       |\n   | Zero-filling (→ NaN)         | 0.269        | 0.158       | 0.051        | 0.014       |\n   | Linear interpolation (LI)    | 0.251        | 0.013       | 0.049        | 0.019       |\n   | Polynomial interpolation (PI)| 0.201        | 0.012       | 0.053        | 0.016       |\n   | GRU-D                         | 0.158        | 0.014       | 0.055        | 0.015       |\n   | NCDE                          | 0.102        | 0.013       | 0.058        | 0.013       |\n   | CSDI                          | 0.088        | 0.012       | 0.048        | 0.013       |\n   | **TST (Ours)**                | **0.065**    | **0.007**   | **0.047**    | **0.012**   |\n\n   **Table 2: Training and imputation time for TST, NCDE, and CSDI on RTX 3090 (batch size fixed)**\n\n   | Dataset | Seq. Len | Ours - TST Train (h) | Ours - TST Impute (s) | NCDE Train (h) | NCDE Impute (s) | CSDI Train (h) | CSDI Impute (min) |\n   |---------|----------|---------------|----------------|----------------|------------------|----------------|--------------------|\n   | Energy  | 24       | **0.67**      | **0.64**       | 2.55           | 2.4              | 1.60           | 35.71              |\n   |         | 96       | **0.80**      | **0.74**       | 7.68           | 5.6              | 5.43           | 135.29             |\n   |         | 768      | **6.67**      | **6.26**       | 60.19             | 56.0              | 84.61          | 2394.71            |\n   | Stock   | 24       | **0.10**      | **0.19**       | 0.67           | 2.0              | 0.18           | 15.40              |\n   |         | 96       | **0.13**      | **0.20**       | 2.45           | 7.2              | 0.40           | 46.78              |\n   |         | 768      | **1.10**      | **1.20**       | 59.32          | 56.0             | 3.55           | 514.08             |\n\n---\n\n4. **“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.”**\n\n    Following the reviewer's suggestions, we replicated the experiment using a real-world dataset, Stocks. For this experiment, we applied a larger convolutional kernel of size $6 \\times 6$ and evaluated two settings: (i) filling irregularities with zeros, and (ii) filling them using natural neighbors. The results on the Stocks dataset mirrored those observed in the synthetic setting, reinforcing our hypothesis about the detrimental effect of \"unnatural\" neighbors.\n\n   Specifically, we trained two diffusion models:  \n   (i) one that predicts the score across the entire image,  \n   (ii) one that predicts only the score corresponding to valid (non-zero) values using masking.\n\n   We evaluated both models by measuring the score estimation loss on the active pixels. The results show that masking yields only a marginal improvement, with losses of **0.81** and **0.79** for setups (i) and (ii), respectively. A visual inspection confirms that the behavior is consistent with the patterns observed in the synthetic experiment. Finally, when applying our proposed approach, which avoids reliance on zero-padding and learns from semantically valid neighborhoods, we observe significantly improved performance with a much lower loss of **0.29** and more consistent kernel behavior.\n\n---\n\n5. **“The paper should provide a more concrete explanation of why the kernel in Figure 1F is considered more consistent compared to Figure 1E”**\n\n    We acknowledge the ambiguity in the original phrasing. Our intention was not to imply that the kernel in Figure 1F is more consistent than the one in Figure 1E, but rather that both 1E and 1F exhibit a similar level of consistency, which is notably improved compared to Figure 1D. Importantly, the kernel in Figure 1F achieves a better quantitative score than 1E, reflecting its superior performance. We will revise the sentence in the manuscript to clearly reflect this distinction.\n\n---"}, {"review_id": "4OWlcckcwM", "reviewer": "Reviewer_zmvt", "summary": "This 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.", "questions": "- 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?", "limitations": "Yes", "rating": 4, "confidence": 1, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "I would like to raise my score to 4 since the authors make hard works in responding most of my questions and most of their answers are satisfying. However, frankly speaking, my main concern to this paper is still not adequately addressed, which is the novelty. I still feel that this paper's main content is more like engineering trick rather than foundational research. If other reviewers or the Area Chair have any concerns and would like to reject this paper, I will not oppose.", "rebuttal": "## Response to Reviewer zmvt\n\nWe appreciate the reviewer's feedback and acknowledge the valuable points raised regarding our work. Below are our detailed responses to the comments and suggestions:\n\n---\n\n1. **“Lack of Novelty in Core Technique: While the proposed method achieves strong results, it largely combines two well-established components... There is no clear technical novelty...”**\n\n    We understand the concern: the components we use are indeed well-established. However, the core contribution of our work lies not in the tools themselves, but in identifying a key failure point in prior approaches and resolving it with a simple, principled solution.\n    \n    Prior methods (KoVAE and GT-GAN) impute missing values and **treat them as originating from the true distribution** during training. We observed that this leads to a significant **drop in generation quality** compared to models trained on fully observed data, a gap caused by **errors in the imputed values propagating through the model**. In practice, it prevents the diffusion from learning the true distribution and thus harms its generation and generalization ability. \n    \n    \n    One of our main contributions is to break this assumption explicitly: **the model sees the imputed values but is never trained to predict them**, unlike KoVAE and GT-GAN. This decoupling of context and supervision is subtle but essential. While it may appear simple, it highlights our novel observation and understanding of the problem, addressing a critical issue in generating content from partial information. Specifically, when missing values are mapped to zeros in the input image, they create “unnatural” neighborhoods that mix valid and invalid information. This can pose challenges for diffusion backbones, particularly U-Nets with convolutional layers, where the kernels are not inherently masked and may inadvertently propagate errors from these artificial neighborhoods. Our approach resolves this by ensuring that the model receives coherent local structure (via imputed neighbors) while ignoring them in the loss, preventing both the propagation of errors and the absence of spatial context.\n    \n    This masking strategy is not just an engineering tweak; it’s a deliberate and non-trivial change to the training objective, grounded in deep problem understanding, architectural reasoning, and empirical results. As shown in Tab. 1 below (Tab. 6 in the paper), we highlight how crucial the combination of our masked loss with natural neighbors truly is. The significant performance gains are *not due to a stronger generative backbone or superior imputation**, but stem directly from this synergy: realistic, coherent inputs paired with loss computed only on observed values. On average across all sequence lengths and missing rates, our method improves the discriminative score by 64.6\\% on Stocks and 56.3\\% on Energy compared to the same pipeline without masking.\n    \n    To summarize, we view the novelty as lying in getting the training dynamics right for irregular time series and in identifying the failure mode that arises when imputed data is naively treated as supervision.\n\n    **Table 1: Averaged Discriminative Scores (30%, 50%, 70% drop) across Sequence Lengths (24, 96, 768)**\n\n    | Model                 | Energy 30% | Stock 30% | Energy 50% | Stock 50% | Energy 70% | Stock 70% |\n    |----------------------|-------------|------------|-------------|------------|-------------|------------|\n    | KoVAE + TST          | 0.340       | 0.173      | 0.360       | 0.176      | 0.403       | 0.139      |\n    | TimeAutoDiff + TST   | 0.297       | 0.103      | 0.333       | 0.102      | 0.465       | 0.420      |\n    | TransFusion + TST    | 0.291       | 0.082      | 0.336       | 0.092      | 0.439       | 0.104      |\n    | Ours (Mask Only)     | 0.361       | 0.170      | 0.347       | 0.294      | 0.429       | 0.372      |\n    | Ours (Without Mask)  | 0.308       | 0.028      | 0.379       | 0.073      | 0.420       | 0.066      |\n    | **Ours**             | **0.116**   | **0.014**  | **0.154**   | **0.019**  | **0.217**   | **0.014**  |\n\n---\n\n2. **“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...?”**\n\n    Previous methods (KoVAE, GT-GAN) used NCDE for imputation. While accurate, NCDE introduces significant practical limitations: it is slow, does not scale to long sequences, and often becomes infeasible on high-dimensional datasets due to the need to compute cubic splines for each channel.\n\n    To address this, we replaced NCDE with TST, which is l**ightweight and scalable**. We then compared TST to various imputation methods, including CSDI, NCDE, GRU-D, linear and polynomial interpolation (**refer to Table 1 in the vxoC reviewer's response**). While TST performed slightly better in our setting (50% drop, length 24), **we do not claim that TST is an optimal imputation tool**.\n\n    In particular, we would like to emphasize that **TST is not the only strategy capable of producing natural neighborhoods**. Strong tools such as NCDE and CSDI also generate smooth, realistic completions that lead to good generative results in our framework. Our choice of TST was not due to performance alone, but primarily due to its efficiency and scalability, both during training and inference.\n\n    To justify this design decision, we added a timing benchmark comparing TST, NCDE, and CSDI across multiple sequence lengths and datasets (**refer to Table 2 in the vxoC reviewer's response**). While all three provide high-quality imputations when combined with our masking approach, TST is consistently faster, often by an order of magnitude, and remains tractable even at length 768, where other methods become extremely slow or fail entirely.\n\n    Furthermore, we note that our method is modular by design: the imputation and diffusion components can be replaced independently in future work, making the framework flexible and adaptable to a wide range of architectures and domains.\n\n---\n\n3. **“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\n    We would like to clarify that the “natural neighborhood” argument is not solely based on visual inspection. In Figure 1, we already report quantitative loss metrics alongside the visualizations, demonstrating a clear improvement for the proposed kernel compared to alternatives. These improved loss values directly support the argument for natural neighborhoods, providing quantitative support in a controlled setup as well as in our extensive empirical evaluation.\n\n    Additionally, we further extended this analysis during the rebuttal phase on the Stocks dataset (please see our response to Q4. and Q5. to Reviewer vxoC), where we observed consistent performance gains, reinforcing the validity of the approach. We will revise the manuscript to make this clearer.\n\n---\n\n4. **“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    We include CSDI in our ablation (**see Tab. 1 in the vxoC reviewer's response**). TST performs slightly better in our setting, but the gap is small. It is likely that in other settings, CSDI could work just as well or better; we make no claim that TST is optimal across the board. Nevertheless, we find TST to be highly efficient in comparison to other baselines, including CSDI, motivating its adoption (**Tab. 1 in vxoC**).\n\n---\n\n5. **“Did the authors consider architectures where masking is applied within the diffusion backbone rather than only at the loss level?**\n\n    Yes, we considered this direction and explored two preliminary variants: (1) modifying the U-Net to apply sparse masking within convolutional layers, and (2) using attention masks within self-attention layers to ignore missing pixels during attention weight computation.\n    \n    We found that both approaches led to lower performance compared to our current strategy, where the diffusion model observes all imputed values as context, but the loss is computed only over the originally observed entries. Quantitative results for this comparison appear in Tab. 2 below.\n    \n    Our hypothesis is that fully excluding missing pixels from the feature computation may limit the model’s ability to leverage useful local structure provided by the imputation module. In contrast, our method retains this context while avoiding error propagation — that is, the risk of reinforcing mistakes in imputed values by treating them as ground truth during training.\n    \n    We also emphasize that implementing masking within the backbone typically requires architecture-specific changes — such as custom handling of sparse kernels or attention masks — which limits generality. Our approach, in contrast, is architecture-agnostic and can be applied directly to any diffusion backbone, including convolutional and transformer-based models.\n    \n    That said, we agree this is an interesting direction. While our initial experiments did not show an advantage, we believe further research may uncover improved implementations that combine the strengths of both strategies.\n\n    **Table 2: Discriminative and predictive scores for different masking strategies on Energy and Stock datasets (sequence length 24, 50% missing rate). Lower is better.**\n\n    | Masking Strategy      | Disc. Energy | Disc. Stock | Pred. Energy | Pred. Stock |\n    |-----------------------|--------------|-------------|---------------|--------------|\n    | Sparse convolutions   | 0.425        | 0.291       | 0.455         | 0.076        |\n    | Attention masking     | 0.375        | 0.191       | 0.405         | 0.063        |\n    | **Ours**              | **0.065**    | **0.007**   | **0.047**     | **0.012**    |\n\n\n---"}, {"review_id": "UcVNGvWvdd", "reviewer": "Reviewer_KWFu", "summary": "This paper proposes a generative modeling method based on ImagenTime that can handle irregular time series data.", "questions": "1. 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?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "The authors clarify most of my concerns including the novelty and difference between the work and ImagenTime.", "rebuttal": "1. **“...It feels that contribution is actually very marginal because most of it is from ImagenTime.”**\n\n    ImagenTime was not designed to handle irregular time series. It assumes fully observed, regularly sampled data, and does not include any mechanism for dealing with missing values or imputation uncertainty. When naively applied to irregular data—either by zero-filling or by completing missing values and training on them as samples from the true distribution—performance degrades significantly.\n\n    To make this concrete, we include an ablation in **Tab. 1 in the vxoC reviewer's response** (Tab. 6 in the paper) comparing several variants. The setup labeled “Ours (W/O masking)” completes the missing values using TST and trains ImagenTime exactly as in the original paper, with no modification to the loss function. The “Ours (Mask Only)” setup fills missing values with zeros and applies masking in the loss. Both approaches perform significantly worse than our full method.\n\n    Specifically, on average across all sequence lengths and missing rates:\n    - Our approach improves the **discriminative score by 64.6% on the Stocks dataset** and **56.3% on the Energy dataset** compared to Ours (W/O Masking).\n    - Compared to Ours (Mask Only), the improvements are **94.0% on Stocks** and **58.3% on Energy**.\n\n    Each of these baselines reveals a different failure mode:\n\n    - In Ours (W/O Masking), the model is trained to match the completed values as if they were true samples. This leads to **error propagation**—any mistake made during imputation is treated as supervision, degrading generalization and generation quality.\n    - In Ours (Mask Only), missing values are replaced with zeros and masked out in the loss. While this prevents error propagation, the model is exposed to **highly unnatural local neighborhoods**, which weakens its ability to learn realistic generative patterns.\n\n    Our method avoids both pitfalls: the model sees **imputed values that form natural, coherent neighborhoods**, but the **loss is computed only on observed values**. This allows the model to benefit from contextual structure while remaining robust to imputation noise—a key factor in the performance gains we observe.\n\n    This ablation decisively shows that the performance gain does not stem from ImagenTime, or any other time-series generator, but from our **specific novel combination** of natural neighbors with masked loss on ground truth data only.\n\n    See also App. B.2 for additional details on our loss function.\n\n---\n\n2. **“There are two types of irregular data that I understand. The first is the data with missing values, and the other is the data with irregular sampling frequency. Does the author's method seem to only address the first scenario?”**\n\n    Our method can address both types of irregularity: missing values and irregular sampling frequency. While we focused our evaluation on missing values, your second scenario can also be accommodated, as we describe below.\n\n    In the case of irregular sampling frequency, we first define a regular time grid by selecting a fixed interval that matches the finest granularity of interest for the application (e.g., 1 second, 1 minute, etc.). This grid redefines the time axis as evenly spaced, and any time points without measurements are treated as missing values (NaNs).\n\n    For example, if the original data contains observations at times *t = 1, 4, 6*, and we choose a fixed interval of 1, we construct a grid:  \n    *t = 1, 2, 3, 4, 5, 6*.  \n    We then place the known values at their respective times, and treat *t = 2, 3, 5* as missing. This allows us to apply our method directly, without modification.\n\n    In summary, by transforming irregular timestamps into a regularly sampled frame with explicit missing entries, our framework can be extended to handle both missingness and irregular temporal spacing in a unified way.\n\n---\n\n3. **“When applying the design of this paper to sequences without missing values, may it lead to performance degradation compared to the original ImagenTime?”**\n\n    When the input data has no missing values, our system naturally **bypasses the completion step and masking**, since all values come from the true distribution. In this case, we simply train ImagenTime as-is, without any modification to its architecture or loss.\n\n    Therefore, **no performance degradation is expected** in the fully observed setting. Our framework is designed to be adaptive: it reduces to ImagenTime in the absence of missingness and enhances it when irregularities are present.\n\n---\n\n4. **“If I understand correctly, it seems that this paper mainly combines KoVAE's stage1 with ImagenTime, am I right?”**\n\n    While both our method and KoVAE use a two-stage structure, **our approach differs fundamentally**:\n\n    - KoVAE uses NCDE completion, which is slow, memory-intensive, and difficult to scale.\n    - We replace this with a **lightweight and scalable TST module** that runs efficiently on longer sequences and higher-dimensional datasets (**see our responses to Reviewers vxoC and zmvt for benchmarks**).\n\n    More importantly, the performance gain in our method is **not simply due to using TST**. The key innovation is the **combination of natural neighbors with a masked diffusion loss**:\n\n    - Unlike KoVAE, we **never treat completed values as ground truth**.\n    - The completed values serve as **context only**, and the **loss is computed solely on the observed entries**.\n\n    This distinction is empirically supported: when we evaluate KoVAE + TST (i.e., using TST for completion but without masking), the performance remains significantly lower than our full method (see Tab. 1 in zmvt for full results).\n\n---\n\n5. **“...the absence of time series in reality has a physical mechanism. 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? Because many missing values occur continuously. If they are only missing at certain time steps, it will be easier to solve them through imputation.”**\n\n    To ensure fair comparison, we follow the **standard protocol** adopted in prior works such as KoVAE and GT-GAN, where missing values are randomly dropped from regular sequences. While it is true that in some cases missingness is driven by physical processes (e.g., sensor downtime), many real-world time series exhibit **irregular, sparse, or stochastic missingness patterns**.\n\n    For instance, in the ERA5 reanalysis dataset (Hersbach et al., 2020), which records temperature over large spatial grids (e.g., 80×80 locations across 120 time steps), missing values frequently occur not due to contiguous outages, but because of sensor availability, asynchronous updates, and communication errors. These lead to random and non-continuous missingness across both space and time.\n\n    That said, our method is **not limited** to random masking patterns:\n\n    - The TST- completion and masked loss mechanism apply equally well to block-wise or structured missingness.\n    - At high masking rates (e.g. 70%), long consecutive NaN segments naturally emerge, even under random masking-effectively simulating structured missingness patterns.\n\n    To address your concern more directly, we ran an additional experiment comparing our model’s performance under two missingness types:\n\n    - (i) 40% randomly dropped values, and  \n    - (ii) 40% continuous missing blocks.\n\n    This experiment was conducted on the Weather & Energy datasets using sequence lengths of 24 and 96. The results (see Tab. 1 below) show comparable performance under both settings—with some cases slightly favoring random drop, and others favoring block-missingness. These results support the robustness of our method across a range of missingness patterns.\n\n    **Table 1: Disc./Pred. scores (↓) for 40% random vs. continuous missingness on Weather (W in the table) & Energy (E in the table) (Len=24, 96).**\n\n    | Len | Metric | W-Rand | W-Cont | E-Rand | E-Cont |\n    |-----|--------|--------|--------|--------|--------|\n    | 24  | Disc.  | 0.057  | **0.053** | **0.081** | 0.082  |\n    |     | Pred.  | **0.021** | 0.025  | 0.047  | **0.043** |\n    | 96  | Disc.  | **0.154** | 0.165  | **0.185** | 0.191  |\n    |     | Pred.  | 0.025  | **0.023** | **0.048** | 0.050  |\n\n\n---\n\n6. **“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\n    We’d like to clarify an important distinction between our architectural design and training strategy. While our framework consists of two components-a TST imputation module and a vision-based diffusion generator, the training is **not purely two-stage**.\n\n    Instead, we use the following setup:\n    \n    - A short warm-up phase (30 epochs) to pre-train the TST using a reconstruction loss.\n    - Followed by joint training of both the imputation and diffusion components.\n\n    This short pre-training helps stabilize learning early on. In our experiments, training fully from scratch often led to poor reconstructions and unstable diffusion training.\n\n    To directly address your question, we compare our method to two alternatives:  \n    (i) **Fully joint training** without any pre-training, and  \n    (ii) **Independent two-stage training** (no joint optimization).  \n\n    As shown in the Tab 2. below, our method outperforms both in discriminative score on both datasets, highlighting the value of our **integrated, pre-trained approach**.\n\n    **Table 2: Avg. discriminative scores @ 30/50/70% drop on Energy & Stock (Len=768).**\n\n    | Model                    | Energy | Stock |\n    |--------------------------|--------|--------|\n    | Joint w/o pretrain       | 0.278  | 0.076  |\n    | Two-stage only           | 0.404  | 0.122  |\n    | **Joint + pretrain (Ours)** | **0.222** | **0.024** |"}, {"review_id": "6zXpNckga2", "reviewer": "Reviewer_ySTA", "summary": "This 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.", "questions": "Q1: 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.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "After the rebuttal, the authors had greatly addressed my concerns and I think this is a good paper.", "rebuttal": "1. **“The comparison is not thorough. The authors may include some VAE-Diffusion methods for comparison.”**\n\n    To the best of our knowledge, only two existing methods combine VAE and diffusion for unconditional time-series generation: TimeLDM and TimeAutoDiff. Since TimeLDM does not provide public code, we were unable to include it in our experiments.\n    \n    To directly adress you concern, we have conducted experemnation with TimeAutoDiff + TST as a representative baseline. In this setup, we replaced the ImagenTime generator in our pipeline with TimeAutoDiff, and used TST to impute missing values prior to generation. The model was trained and evaluated under the exact same protocol as ours. To ensure the performance gains of our method are not tied to a specific generative architecture, we further included TransFusion + TST, a strong diffusion model that operates directly on time-series data without relying on image representations. This helps isolate the impact of our contributions from the backbone architecture itself. We attach below the table results. We will add this new experimentation to the final revision.\n\n    As shown in Tab. 1 below, both TimeAutoDiff + TST and TransFusion + TST underperform significantly across all datasets, sequence lengths, and missing rates. These results highlight that our improvements do not stem from using a more powerful generator or imputer, but from our core innovation: combining natural completions with a masked loss applied only to observed values. This principled design leads to consistent and substantial gains over all tested alternatives.\n\n\n    **Table 1: Avg. Disc. Scores @ 30/50/70% drop (Len=24/96/768). E = Energy, S=Stock**\n\n    | Model              | E30  | S30  | E50  | S50  | E70  | S70  |\n    |-------------------|------|------|------|------|------|------|\n     | KoVAE + TST       | 0.340 | 0.173 | 0.360 | 0.176 | 0.403 | 0.139 |\n    | TimeAutoDiff + TST| 0.297 | 0.103 | 0.333 | 0.102 | 0.465 | 0.420 |\n    | TransFusion + TST | 0.291 | 0.082 | 0.336 | 0.092 | 0.439 | 0.104 |\n    | Ours (Mask Only)  | 0.361 | 0.170 | 0.347 | 0.294 | 0.429 | 0.372 |\n    | Ours (No Mask)    | 0.308 | 0.028 | 0.379 | 0.073 | 0.420 | 0.066 |\n    | **Ours**          | **0.116** | **0.014** | **0.154** | **0.019** | **0.217** | **0.014** |\n\n\n---\n\n2. **“..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..”**\n\n    We appreciate the reviewer’s suggestion to further justify our design choices. Our architectural decisions are grounded in detailed empirical analysis and targeted error investigations of prior approaches, which revealed two key limitations:\n\n    - NCDE-Based Imputation Challenges: Prior work that utilizes NCDE for imputation, such as KoVAE, suffers from scalability issues—NCDE becomes computationally infeasible for long sequences and introduces substantial overhead even for short to mid-length time series.\n\n    - Lack of Masking in Imputation Pipelines: Methods that omit masking during the imputation process (e.g., using a deterministic completion step as input to diffusion) risk encoding inaccurate or biased reconstructions. The diffusion model in such cases learns from data distributions that do not reflect the underlying true data, which leads to compounding errors during generation.\n    \n    Our ablation studies at Tab 1. below directly support these observations. Specifically:\n    \n    - Masking-Only and Ours (W/O Masking) configurations fail to accurately capture the ground-truth distribution, underscoring the importance of masking during diffusion.\n    \n    - Simply replacing NCDE with TST in KoVAE improves computational efficiency but does not significantly improve performance, highlighting that the choice of completion model alone is not sufficient.\n    \n    - Even when employing strong diffusion models like TransFusion with TST-based completion, performance degrades notably without masking.\n    \n    In contrast, our proposed architecture-combining TST completion with strategic masking during diffusion-achieves clear gains across all tasks. This combination allows the diffusion model to model uncertainty effectively and prevents over-reliance on potentially inaccurate imputations. Our findings thus demonstrate that the interplay between completion and masking is critical, and neither component alone explains the performance improvements. This design is not arbitrary but directly motivated by both theoretical considerations (avoiding error accumulation via inaccurate inputs) and empirical results.\n\n---\n\n3. **“...Could the authors provide more details about the classifier used to compute this score?”**\n\n    To ensure **fair and meaningful comparisons with prior work**, we used the same pre-trained classifier employed by baseline methods such as GT-GAN and KoVAE.\n\n    This classifier is a task-specific model trained to distinguish between real and synthetic sequences using supervised learning on labeled data. In our case, the classifier was trained exclusively on real and ground-truth-labeled data and then evaluated on a balanced test set containing both real and generated samples.\n    \n    To evaluate its fidelity-measuring capability, we ensured that the classifier:\n\n    - Shows near-zero discriminative power when evaluated on real vs. real samples drawn from the same distribution (e.g., original data vs. shuffled batches), confirming that the classifier does not detect spurious differences in such control setups.\n    - Demonstrates consistent performance across multiple random seeds and datasets.\n    - Is not overfitting to a specific generation method, but generalizes well across different types of synthetic data.\n\n---\n\n4. **“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...”**\n\n    Thank you for pointing this out. We agree that at first glance, increasing the noise level from 0.1 to 0.15 may seem like a relatively small change. However, the impact on predictive performance is more pronounced due to the scale of the underlying data.\n\n    In our experiments, the noise was added as additive Gaussian noise sampled from a normal distribution N(0, σ), where σ corresponds to the specified noise level (e.g., 0.1 or 0.15). Importantly, this noise is added independently of the original data distribution or scale.\n\n\n    For example, in the Energy dataset the standard deviation of the target variable is approximately 0.22. Therefore, adding noise sampled from N(0, 0.15) introduces noise with a standard deviation that is about 68% of the data's inherent variability. This means that the relative magnitude of the noise is substantial and not minor, as the absolute values might suggest.\n\n    Thus, the observed degradation in predictive performance is consistent with the relatively large proportion of noise added compared to the signal. We will clarify this point in the revised manuscript for better transparency.\n\n---\n\n5. **“Existing time series generation methods also include the combination of VAE and Diffusion. How is the method compared with these?”**\n\n    Answered in 1.\n\n6. **“Can the method be applied to time-series data with categorical and numerical attributes?”**\n\n    In our paper, we followed benchmarks from previous work, which focused on numerical datasets only. To improve evaluation and show broader applicability, we added 7 new datasets and 4 sequence lengths. In response to your question, we further extended our method to support mixed-type time series with both categorical and numerical attributes. Our approach treats categorical variables using learnable embeddings that are jointly optimized during training. Specifically, we replace each categorical feature with a dense embedding vector, transforming the input from mixed data types into a unified continuous representation. For example, a time series with 5 numerical features and 3 categorical features becomes a fully continuous representation where categorical indices are mapped to learned embedding vectors that are concatenated with the numerical features.  \n    The key advantage is that these embeddings are learned end-to-end with the generative model, allowing the model to find the optimal representations for categorical values that best capture their relationships within the temporal context. The diffusion model then operates entirely in this continuous embedding space, naturally handling both numerical dynamics and categorical dependencies.  \n    During generation, we apply a simple postprocessing step to map the generated embedding vectors back to discrete categories by finding the nearest learned embedding or using classification heads, depending on the setup.\n    \n    As part of this extension, we evaluated our method on the Traffic dataset—which includes both categorical and numerical features—to demonstrate this capability [1]. As shown in Tab. 2 below, our method achieved the best discriminative scores across all missing rates.\n\n    [1] Hogue, J. (2019). Metro Interstate Traffic Volume [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5X60B.\n\n    **Table 2: Disc. scores on Traffic @ 30/50/70% drop (Len=24, 96).**\n\n    | Seq Len | Model  | 30%   | 50%   | 70%   |\n    |---------|--------|--------|--------|--------|\n    | 24      | GT-GAN | 0.481  | 0.473  | 0.485  |\n    |         | KoVAE  | 0.154  | 0.172  | 0.222  |\n    |         | **Ours** | **0.061** | **0.064** | **0.087** |\n    | 96      | GT-GAN | 0.493  | 0.491  | 0.488  |\n    |         | KoVAE  | 0.212  | 0.245  | 0.307  |\n    |         | **Ours** | **0.073** | **0.091** | **0.102** |"}]

Author rebuttal:

Meta-review: This submission proposes a generative model for irregular time series. The method first completes missing values using a Time Series Transformer (TST) to create more natural temporal neighborhoods, and then using a vision-based diffusion model (ImagenTime) with masking to generate realistic time series. The underlying intuition of this approach is to overcome the issue of unnatural neighborhoods caused by zero-padding. Empirical results demonstrate the improved performance, and also improved training efficiency.

The reviewers and I myself agree that this is indeed an important and practical problem. The proposed method seems reasonable, and the empirical performance seems advantageous across a wide range of datasets and tasks. The authors (and the reviewers) also elaborated a lot in the rebuttal period, and more comprehensive results are provided, which also seem supportive.

Nevertheless, most reviewers have mentioned the concern of limited novelty, as the framework mainly combines existing components (time series completion via TST, and diffusion-based generation via ImagenTime) without introducing fundamentally new architectures or theoretical insights.
I do agree with this situation and also expect more exciting novel components/analysis, but I tend to believe that a comprehensive empirical study on a not-to-involved strategy that seems to work well also accounts a reasonable contribution, and could be inspiring to the community. Therefore, I'm leaning towards acceptance.
Decision: Accept (poster)