Title: Breakthrough Sensor-Limited Single View: Towards Implicit Temporal Dynamics for Time Series Domain Adaptation
Venue: NeurIPS 2025
Accepted: yes
Keywords: time series; domain adaptation; implicit temporal dynamics
Abstract: Unsupervised domain adaptation has emerged as a pivotal paradigm for mitigating distribution shifts in time series analysis. The fundamental challenge in time series domain adaptation arises from the entanglement of domain shifts and intricate temporal patterns. Crucially, the latent continuous-time dynamics, which are often inaccessible due to sensor constraints, are only partially observable through discrete time series from an explicit sensor-limited single view. This partial observability hinders the modeling of intricate temporal patterns, impeding domain invariant representation learning.  To mitigate the limitation, we propose **EDEN** (multiple **E**xplicit **D**omain **E**nhanced adaptation **N**etwork), expanding the raw dataset to multi-scale explicit domains, multi-subspace explicit domains and multi-segment explicit domains. EDEN enhances domain adaptation with three coordinated modules tailored to integrate multiple explicit domains: (1) Multi-Scale Curriculum Adaptation implements progressive domain alignment from coarse-scale to fine-scale. (2) Quality-Aware Feature Fusion evaluates feature quality in multi-subspace explicit domains and adaptively integrates temporal-frequency features. (3) Temporal Coherence Learning enforces segment-level consistency with multi-segment explicit domains. The representation enriched by multiple explicit domains bridges the gap between partially observed discrete samples and the underlying implicit temporal dynamics, enabling more accurate approximation of implicit temporal patterns for effective cross-domain adaptation. Our comprehensive evaluation across 6 time series benchmarks demonstrates EDEN's consistent superiority, achieving average accuracy improvements of 4.8% over state-of-the-art methods in cross-domain scenarios. Code is available at the anonymous link: 
 <https://github.com/mingyangliu1024/EDEN>.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes EDEN, a method for time series unsupervised domain adaptation (UDA) that introduces multiple explicit domains—multi-scale, multi-subspace, and multi-segment—and integrates them via three coordinated modules to better model implicit temporal dynamics under partial observability. The method is evaluated on six benchmarks and shows consistent improvements.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper addresses an important and challenging problem in time series domain adaptation.\n2. The overall writing is clear and well-structured.\n3. The proposed framework is systematic and incorporates several components aimed at enhancing representation learning.\nWeaknesses:\n1. The novelty is somewhat limited: the proposed modules (curriculum learning, adaptive fusion, segment-level consistency) are composed of well-known ideas, adapted to the time series domain without clear methodological breakthroughs.\n2. The use of \"explicit domains\" is largely terminological; it is unclear how fundamentally this differs from multi-scale or multi-view learning.\n3. While the reported performance gains are consistent, they are relatively modest (4.8% on average), and the experimental section lacks strong ablations to clarify the contribution of each module.\n4. The model architecture is relatively complex, but there is limited analysis of computational cost or inference efficiency, which may impact real-world applicability\n5. The method is evaluated only on classification tasks; it's unclear whether the proposed approach generalizes to other important TSDA settings such as forecasting or anomaly detection.\n6. The paper lacks a clear comparison with more recent time-series specific UDA baselines beyond general-purpose UDA methods.\n7. The paper does not clearly justify why the combination of these three strategies is particularly effective or necessary, as opposed to using one or two of them.\n\nQUESTIONS:\n1. Can EDEN be extended to other TSDA tasks such as forecasting or anomaly detection, beyond classification?\n2. Is the combination of all three modules necessary, or could a simpler subset achieve comparable performance?\n3. How does the notion of “explicit domains” differ fundamentally from existing multi-view or multi-scale learning frameworks?\n\nLIMITATIONS:\n1. The proposed modules are largely based on established ideas, with limited methodological novelty.\n2. The concept of “explicit domains” is not rigorously defined and may overlap with prior terminology.\n3. Performance gains, while consistent, are relatively modest, and stronger ablation studies are needed to validate design choices.\n4. The method’s computational cost and scalability are not thoroughly analyzed, which may impact practical deployment.\n5. Evaluation is limited to classification; generalization to other TSDA tasks remains untested.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes EDEN, a novel framework for domain adaptation (UDA) in time series classification. The key innovation lies in expanding the raw dataset into multiple explicit domains, i.e. multi-scale, multi-subspace, and multi-segment, to better capture implicit temporal dynamics. It integrates these domains through three coordinated modules: MSCA to align domains progressively from coarse to fine scales; QFusion to combine temporal and frequency features based on their discriminative and transferable qualities; TCL to enforce consistency across adjacent segments.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. EDEN explicitly models multi-scale, multi-subspace, and multi-segment domains in time series domain adaptation, addressing the limitations of single-view sensor data.\n2. The integration of temporal-frequency features with adaptive fusion (QFusion) combines these features based on their discriminative and transferable qualities. \n3. The paper provides a theoretical Proposition of the Domain Adaptation Bound and covers a wide range of time series datasets, outperforming baselines.\n\n**Weaknesses**\n\n1. **Scalability** - The author discusses the limitations of single-view sensor data, experiments lack validation in low-resource scenarios, e.g., only a few data points in the source dataset, like few-shot learning. Testing EDEN’s ability to leverage multiple domains to compensate for scarce source data would strengthen robustness claims.\n2. Some time series representation learning works in the time series domain adaptation is not included in the related work and experiments, refer to question 2. \n3. No visualizations illustrate how multi-domain alignment and time-frequency fusion improve representations. The author should provide some case studies to show the improvement in these designed modules.\n\nQUESTIONS:\n1. **Generalization ability** - Could EDEN be evaluated on single-dataset time series classification (without domain shifts) to verify its generalized feature extraction capability?  Could EDEN be tested in few short learning experiments to show its robustness and generalization ability?\n2. **Baseline comparison** - The representation learning is also an effective solution for domain adaptation in time series, e.g., TS-TCC and TimeMAE. The author might add these works to the related work discussion and compare them to validate EDEN’s competitiveness.\n3. **Reference Typo** - Duplicate references in Lines 378 and 380.\n4. Could you provide some visualizations of representations to show the gap between the source data and target data and how the EDEN to improve the representation learning in different modules, especially including representations in explicit domains (a), domain alignment (b), and feature fusion (c).\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper find a unique perspective of TSDA and therefore proposes three strategies to model this orginal dataset, i.e., multi-scale, multi-space and multi-segment modeling. The exp results seems ok for me.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1）The overall motivation is sound. Since different time series data can exhibit diverse explicit patterns, modeling them from multiple perspectives is a reasonable and compelling direction.\n\n2）The results are promising, achieving state-of-the-art performance compared to a recent NeurIPS 2024 method.\n\nWeaknesses:\n\n1）The abstract and introduction lack clarity in conveying the core ideas behind \"multi-scale\" and \"multi-subspace\" modeling. For instance, the role and mechanism of Quality-Aware Feature Fusion and Multi-Scale Curriculum Adaptation could be summarized more clearly. Currently, I have to dive into the full method section to grasp how it works. A concise explanation or intuitive example early on would significantly improve readability.\n\nQUESTIONS:\n1）Curriculum Learning Justification\nThe paper assumes coarse-scale features are easier and more domain-invariant, motivating the coarse-to-fine curriculum. However, this assumption needs clearer theoretical or empirical support.\n\n2）Handling Different Time Scales\nThe method assumes source and target share at least comparable temporal resolutions. It is unclear how the model performs when the target has a very different time scale. Clarification or discussion of robustness in such scenarios is needed.\n\n3）Limited Explicit Domain Perspectives\nOnly three types of explicit domains are explored. Could other forms—e.g., modality, condition, or context—be useful? A brief discussion on unexplored directions would improve completeness.\n\n4）Module Naming Consistency\nThe module names are somewhat inconsistent. Consider the following renames to better match Multi-Scale Curriculum Adaptation and improve conceptual clarity.\n\nQuality-Aware Feature Fusion → Quality-Aware Multi-Subspace Attention\n\nTemporal Coherence Learning → Coherence-Aware Multi-Segment Alignment\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 1, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-04"}