Title: Adaptive Time Encoding for Irregular Multivariate Time-Series Classification
Venue: NeurIPS 2025
Accepted: yes
Keywords: Irregular sampling

Time encoding

Attention mechanism

Consistency regularization

Multivariate time-series classification
Abstract: Time series are often irregularly sampled with uneven time intervals. In multivariate cases, such irregularities may lead to misaligned observations across variables and varying observation counts, making it difficult to extract intrinsic patterns and degrading the classification performance of deep learning models. In this study, we propose an adaptive time encoding approach to address the challenge of irregular sampling in multivariate time-series classification. Our approach generates latent representations at learnable reference points that capture missingness patterns in irregular sequences, enhancing classification performance. We also introduce consistency regularization techniques to incorporate intricate temporal and intervariable information into the learned representations. Extensive experiments demonstrate that our method achieves state-of-the-art performance with high computational efficiency in irregular multivariate time-series classification tasks.


Peer reviews:
comments: SUMMARY:
This paper proposes an adaptive time encoding method that generates latent representations at learnable reference points to improve irregular multivariate time-series classification. The approach enhances performance by capturing missingness patterns and incorporating temporal-intervariable relationships through consistency regularization.

STRENGTHS AND WEAKNESSES:
Strengths: The paper is well-structured overall, with clear descriptions and rigorous analysis.
Weaknesses: Some parts lack sufficient detail or justification, which will be listed in the " Questions" section.

QUESTIONS:
1. The Adaptive Time Encoding (ATE) proposed in the paper uses a set of learnable reference time points, but it does not provide details on how the hyperparameter K is determined. Does the choice of K significantly affect classification performance? If the target dataset has a different time distribution from the training set, do the learned reference time points still generalize well?
2. ATE uses a multi-head temporal attention mechanism for interpolation. However, in extremely sparse scenarios, this process may suffer from insufficient observation points, leading to uneven or even ineffective attention weight distributions. When the number of observations for an input variable is very limited, is the interpolation mechanism still effective?
3. Among the comparison methods, only one is from 2024. Are there any other more recent methods that could be included for comparison?
4. For Figure 6, could the raw data be provided as a reference? This would make the results more convincing.
5. The meanings of the horizontal and vertical axes in Figure 7 do not appear to be explained.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-06-13

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors address the problem of classifying irregular time \nseries with missing values (IMTS). They propose a model \nthat for each channel interpolates the observations to a\nreference time points, that are initialized in regular intervals,\nbut then can be updated. Cross attention is used for\nthe interpolation. Finally latent representations are\nadded across attention heads and channels and the class\nprobabilities regressed on this. The loss is combined with\n- i) a contrastive loss for the reference point representations\n  of original instances and masked instances vs. other\n  instances,\n- ii) a contrastive loss for the reference point representations\n  of original instances and masked instances vs. other\n  reference time points (of the same instance), and\n- iii) a cross entropy loss between channels.\n\nIn experiments on four datasets against many baselines\nthey show that their model outperforms existing \nmethods.\n\nSTRENGTHS AND WEAKNESSES:\nstrong points:\n- s1. plausible and simple model: interpolate channels to an adaptive\n  list of reference points.\n- s2. good, mostly consistent lifts over the baselines.\n\nweak points:\n- w1. hyperparameters maybe trained on test?\n\nad w1. hyperparameters maybe trained on test?\n- how did you optimize your hyperparameters? \n  in appendix I you say \" Here, we report classification \n  performance using the AUPRC, which was used to \n  select the best models for each dataset.\" (line 635).\n  Does this mean you optimized your hyperparameters\n  on test? If so, would your results still stand if you had\n  to pick them on a validation sample within train?\n  Are the hyperparameters of the baseline models \n  also optimized on test?\n\nQUESTIONS:\n- q1. how did you optimize your hyperparameters? (w1)\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes ATENet, an end-to-end encoder-classifier framework for irregular multivariate time-series classification. The key contribution is a novel Adaptive Time Encoding (ATE) module that learns reference time points to generate latent representations while capturing irregular sampling patterns and missingness. To further enhance the learned representations, the framework introduces two regularization terms: temporal consistency (capturing fine-grained temporal dynamics) and intervariable consistency (modeling dependencies across variables efficiently without graph neural networks). The proposed method is evaluated across multiple real-world benchmarks and demonstrates strong performance and computational efficiency compared to state-of-the-art methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.The paper identifies two concrete challenges in modeling irregular multivariate time series—(1) failure to utilize missingness patterns, and (2) neglect of inter-variable correlations—and addresses each with dedicated technical components.\n2.The method is tested on diverse datasets (healthcare and activity monitoring), and the experiments span multiple evaluation setups including robustness to missing variables and computational efficiency.\n3.The paper is easy to follow, with clear visualizations and ablation studies that support the claims.\n\nWeakness:\n1.  While the paper includes ablation studies, it remains unclear which component (adaptive reference points, temporal consistency, or intervariable consistency) contributes the most in specific cases. \n2. The paper combines known ideas (learnable queries, contrastive consistency, and interpolation attention) in a cohesive way, but may feel more incremental rather than fundamentally new to readers familiar with this area.\n\nQUESTIONS:\nPretraining vs. End-to-End: Have you considered comparing ATENet with pretrain-then-finetune frameworks (e.g., PrimeNet)? Your method does representation learning implicitly; how does it perform against explicit representation learning approaches?\n\nLIMITATIONS:\nThe storyline, challenges and corresponding technical component, is complete, but to somehow incremental and not exciting.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the challenging temporal classification task with irregularly measured time-series as inputs. The authors propose a novel interpolation-based encoder-classifier structure, ATENet, with learnable reference points for the time-series embedding. In addition to the gradient from classification loss, two additional regularization terms are introduced in ATENet to enhance the representation quality of embeddings from the encoder. Specifically, with randomly masked data points, the instance-level and point-level contrastive losses are utilized to encourage the encoder to effectively capture unique temporal characteristics of the input time-series. In the meantime, the consistency loss enables ATENet to preserve temporal dependency structure of the input in its latent embeddings in the sense of outer products. The high-quality time-series representation learned by the encoder of ATENet enables a straight-forward design of the classifier network which greatly contributes to the improved computation efficiency compared to transformer- or graph-based approaches. Experimental results on three real-world datasets show that ATENet achieve better time-series classification accuracy over multiple state-of-the-art approaches. The authors also provide detailed ablation study to demonstrate the effectiveness of their different design choices in ATENet.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- ATENet enables efficient processing of irregularly measured time-series through interpolation at key reference points and fixed length representation of predictive temporal patterns. Further, the reference points could be flexibly learned based on the training data instead of being manually designed which usually requires domain knowledge and expertise.\n- The usage of positional encoding of observation time $t$ and reference point $r$ as key and query in attention mechanism is interesting and might be potentially equivalent to the concept of dynamic time warping (DTW) which is well-known to be effective for comparison of time-series of variable lengths.\n- The notion of temporal consistency between the latent embedding $z$ and input time-series $x$ is sort of novel, and the design of corresponding regularization terms indeed have huge contribution to the classification performance according to the ablation study results in Table 3. The high-quality latent representation also enables a simplified classifier model, which contributes to the improvement in computational efficiency of ATENet compared to other models.\n\nWeaknesses\n- The fundamental assumption on the sparsity of the time-series data is not explicitly discussed. For instance, for meaningful interpolation of the irregularly measured time-series in Eq. 4, the sampling frequency of the input signals should satisfy certain criteria. If the measurement is overly sparse, ATENet may fail to distinguish different input time-series due to the lack of effective data points.\n- The authors should also discuss any potential inductive biases introduced by the attention-based interpolation described in Section 3.2.\n- It is unclear if the set of reference points $\\mathbf{r}$ is globally shared by all samples or dynamically computed for individual input time-series. The authors need to justify for a global set of reference point $\\mathbf{r}$ if applicable.\n- Relevant study applying Fourier transform and spectral analysis should be discussed in literature review, and there should be additional baselines (utilizing frequency domain representations) from these researches.\n- The performance gain of AETNet seems to be marginal except for AUPRC on P12-M and PAM dataset.\n\nQUESTIONS:\n1. What is the implicit assumption on time-series sparsity in AETNet? Is AETNet applicable to any time-series data?\n2. Are the missing patterns always important to time-series classification? What is the performance of AETNet on datasets with missingness at random?\n3. Does the time-series interpolation in Eq. 4 introduce inductive biases to AETNet? Would it be affected by noisy signals?\n4. Is the set of key reference points $\\mathbf{r}$ globally shared by all samples in a dataset? What is the impact of this design?\n5. Is the outer product in Eq. 10 sufficient to model complex temporal patterns and dependencies across time steps, especially when the time-series are irregularly measured?\n6. There should be additional baselines with time-series embeddings in the frequency domain.\n7. ATENet consistently has lower performance than MTSFormer on P19 datasets according to Table 1 and Figure 4. There should be analysis on the underlying reasons and potential limitations of ATENet.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}