Breakthrough Sensor-Limited Single View: Towards Implicit Temporal Dynamics for Time Series Domain Adaptation
Authors: Mingyang Liu
Xinyang Chen
Xiucheng Li
Weili Guan
Liqiang Nie
Venue: NeurIPS 2025 poster ()
Keywords: time series; domain adaptation; implicit temporal dynamics
Primary area: other

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>.

Breakthrough Sensor-Limited Single View: Towards Implicit Temporal Dynamics for Time Series Domain Adaptation
Mingyang Liu', Xinyang Chen la Xiucheng Li', Weili Guan', Liqiang Nie!
School of Computer Science and Technology. Harbin Institute of Technology (Shenzhen)
School of Information Science and Technology. Harbin Institute of Technology (Shenzhen) (mingyangliu1024, chenxinyang95, nieliqiang)0gmann com, {lixiucheng, guanseili) edu.
Abstract
Unsupervised domain adaptation has emerged as pivotal paradigm for mitigat- ing distribution shifts in time series analysis. The fundamental challenge in time series domain adaptation arises from the entanglement of domain shifts and in- tricate temporal patterns. Crucially, the latent continuous time dynamics, which often inaccessible due sensor constraints, are only partially observable through discrete time series from an explicit sensor-limites 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 Explicit Domain Enhanced adaptation Network), 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 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 adapta- tion. 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 http///iggaggnlu2//////////
Introduction
Time series classification has been studied with immense interest in extensive applications and has made significant progress [15, 40, 37]. Nevertheless, practical deployment the models often encounters severe performance degradation caused by distribution shifts between training and testing environments [27]. Unsupervised domain adaptation (UDA) [21, 12], leveraging knowledge transfer from related source domain to the unlabcled target domain, can be promising solution.
The core challenge time series domain adaptation (TSDA) stems from the entanglement of domain shifts and intricate temporal patterns, which is further compounded by the partial observability of latent continuous-time dynamics. Unlike images or text, where raw observations capture rich semantic information, time series data constitutes explicit sensor-limited single domain, whose discrete
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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observations are constrained by acquisition parameters (e.g., sampling rates or record durations) that only partially reflect the underlying implicit domain of continuous- time processes [10], and hinder the modeling of intricate temporal patterns. Critically, modifying these sensor constraints, yields different explicit domains, each emphasizing distinct aspects of the implicit temporal dynamics that must be effectively captured. Conventional approaches relying on a single explicit domain exhibit limited representational modeling capabilities, impeding domain invariant representation learning.
Current advanced methods primarily focus on extracting temporal representation directly from raw observational data [38, 25]. demonstrating limited effectiveness in simultaneously capturing intricate temporal patterns and addressing their associated domain shifts. While recent advancements incor- porating representations from frequency subspace show improved domain adaptation performance [14, 18], these approaches remain fundamentally constrained by fixed temporal -requueny integration paradigms. To unravel implicit temporal dynamics and enhance domain adaptation, is essential to break through the limitations of the single view in the raw dataset.
In light of the above motivations, we propose EDEN (multiple Explicit Domain Enhanced adaptation Network) for TSDA based on the integration of multiple explicit domains. By expanding the restrictions slightly, we expand the original dataset (1) multi-scale explicit domains at fine-scale and coarse-scale, (2) multi-subspace explicit domains containing temporal subspace and frequency subspace, and (3) melli-segment explicit domains with nearby segments. Furthermore, EDEN investigates interactions among multiple explicit domains and achieves their effective integration with three coordinated modules: (1) For multi-scale explicit domains, we highlight that the coarse-scale features manifest smaller domain discrepancy, which is the metric proposed in domain adaptation theory [2]. Based on that, Multi-Scale Curriculum Adaptation is proposed to progressively align the source and target domain from coarse-scale to fine-scale. This curriculum learning strategy stabilizes global feature alignment before refining local discriminating details. (2) For multi-subspace explicit domains, we reveal that the discriminative capability of models for the same class may vary significantly between two subspaces. Based on that, we propose Quality-Awar Feature Fusion, an adaptive fusion mechanism that weighs subspace contributions based on their representation quality for specific instances. (3) For multi-segment explicit domains, we identify that nearby segments inherently possess similar class-related semantic information. Based on that, we propose Temporal Coherence Learning, encouraging the model to exhibit consistent and stable behavior on adjacent temporal windows. Main contributions
follows:
1. Going beyond previous methods, we break through the sensor-limited single view and expand to multiple explicit domains, taking advantage of rich semantic information and comprehensive reflection of domain shift from multiple explicit domains, unraveling implicit temporal dynamics.
2. We propose EDEN, which integrates multiple explicit domains and enhances TSDA in three coordinate modules: Multi-Scale Curriculum Adaptation to align the source and target domain from coarse-scale to fine-scale; Quality-Awart Feature Fusion to adaptively integrate temporal- frequency features; Temporal Coherence Learning to encourage consistent and stable behavior.
EDEN achieves average accuracy improvements of 4.8% over state-of-the-art methods across a wide range of time series datasets in cross-domain scenarios.
2
Related Work
General Unsupervised Domain Adaptation Unsupervised domain adaptation leverages the la- beled source domain predict the labels of different but related, unlabeled target domain. It has a wide range of applications [41, 42, 13]. To achieve this, UDA methods aim to minimize the domain discrepancy and thereby decrease the upper bound of the target error [2]. Existing UDA methods can be classified into three categories: (1) Methods based on adversarial training introduce a domain discriminator to distinguish source samples from target ones, while the feature extractor learns domain- invariintn representations to fool the domain discriminator. Advanced methods include DANN [12], CDAN [22] and DIRT-T [31]. (2) Methods based on statistical divergence aim to extract transferable features by minimizing statistical domain discrepancy in a latent feature space. Widely used methods include DAN [21], DoepCoral [33] and HoMM [6]. (3) Methods based on self-training assign pseudo-labels on unlabeled target data and select confident samples to combine with source samples in the next iteration of training. Widely used methods include PFAN[7], CST [17] and AdaMatch [4]. Overall, these methods are generally designed. Although these methods can be

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applied to time series through tailored feature extractors, they often yield suboptimal performance due to neglecting the unique characteristics of time series.
Unsupervised Domain Adaptation for Time Series To date, limited methods have been tailored to unsupervised domain adaptation for time series. Early works focus on modeling features in the temporal subspace. VRADA [26] and CoDATS [38] consider suitable feature extractors based on the temporal structure. SASA [5] adopts LSTM [30] to capture the domain-invarian association. AdvSKM [19] adapts MMD [34] to fit time series characteristics. CLUDA [25] learns contextual representation via contrastive learning. Recently, several works have highlighted the necessity of simultancously modeling features in the temporal and frequency subspace for UDA. RAINCOAT [14] firstly introduces frequency features into domain adaptation, aligning temporal features and frequency features respectively via Sinkhorn divergence. ACON [18] proposes mutual learning and adversarial learning in frequenly subspace. Despite remarkable progress, existing methods fail to exploit richer semantic in potential explicit domains, restricting domain shift mitigation.
3 Multiple Explicit Domains of Time Series
3.1 Problem Setup
In this paper, we study Unsupervised Domain Adaptation (UDA) problem for time series classification. Discrete time series are often a series of data points obtained by observing a continuous- time process at discrete sequence of equally spaced points in time [10]. In time series classification problems, the dataset can be formalized as from i-th continuous- -imm process pt, containing observation of C variates over T time steps.
((): n where i-th raw sample r; E TR CxT sampled
In UDA setup, we are given n, raw labeled samples P = {() drawn from the source distribution P and raw unlabeled samples Q {r))) drawn from the target distribution Q. Due to the domain shift between the source and target distribution, the model trained only on labeled source data encounters severe performance drops when deployed in the target domain. UDA for time series classification aims to learn time series classification model with labeled source sample set P and unlabeled target sample set Q, which can make accurate predictions on the target domain.
3.2 From Raw Time Series to Multiple Explicit Domains
Discrete time series datasets inherently include two explicit restrictions: sampling rate and record duration. The sampling rate determines how frequently observations are made, impacting the resolution and fidelity of captured data. The record duration defines the temporal extent, influencing the model's ability to capture trends and patterns within segments. Given i-th continuous-time process Ps, we obtain different samples by modifying the sampling rate and record duration. In mathematics, the continuous-time process set = {pr forms an implicit domain, which inaccessible due to sensor limitations. With explicit restrictions, the raw input set [r:} forms an explicit domain.
Existing domain adaptation methods tailored for time series are mostly limited domain. By expanding the restrictions slightly, we treat frequency data as an explicit domain under the restriction of cmmpora-ffrequeccy transformation. To break through the limitations of the single view in the raw dataset and comprehensively reflect implicit temporal dynamics, we expand the original dataset into multiple explicit domains from three perspectives: record duration, sampling rate, and comporal-frqueent transformation. As shown in Figure 1(a), given raw time series dataset (ra)e for each instance r, we expand three kinds of explicit domains:
single explicit
(1) Given a raw sample r, we segment it and obtain the set of segments {x, 1K where x, is sub-series of r or r itself. The segments in {x,} may have different length. After this step, each r in the original dataset is expanded to include (x, in the multi-segment explicit domains.
(2) Given a segment Xj, we downsample x, with a coarser scale M, and obtain the coarser-scale data x The segment x, contains finer-scale information, denoted as x/. Each raw sample is expanded to include (x/, x x in the multi-scale explicit domains.
3

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Multi-Scale Curriculum Adaptation
L
Quality-Aware Feature Fusion
Barth
91
?p
Pawrt
Fusion
XX
MSCA
(+) Multiple Explicit Domains X.
Sugment (X. /
Muti-Sale Ala
r
(d) Temporal Coherence Learning
stetAty)) Crer. lagl betett (ia))
FFT
PT
Down-sampling
Temporal Segment
MSCA
MSCA
Mutdi Subspace
input r;
X, (R
Figure I: The schematic of EDEN, enhancing TSDA with expanded multiple explicit domains and three coordinated modules tailored to integrate multiple explicit domains. (a) Expanding the raw inputs into multiple explicit domains from three perspectives: record duration, sampling rate, and temporal-frequeee
transformation. (b) Multi-Scall Curriculum Adaptation implements progressive domain alignment through multi-scale explicit domains. (c) Quality-Award Feature Fusion evaluates feature quality and adaptively integrates comporal-frequenee features. (d) Temporal Coherence Learning enforces segment-level consistency constraints via multi-segment explicit domains.
(3) Given a fine-scale data x and corresponding coarse-scale data x transform xf and x into the frequency subspace by Fast Fourier Transform, resulting complex variables v and vf. Each raw sample r is expanded to include {(x/ x5. v vj) the multi-subspaac explicit domains.
To simplify, multiple explicit domains
[X:) derived from raw dataset {r:) are formalized as:
X 1== X, X (x vl. v..g).
The above three steps do not alter the relevant category concepts, and thus X; share the same ground-truth label with r,. With the expanded multiple explicit domains, we have the labeled source domain P = {(X,,) y?) and the unlabeled target domain Q {X)), Superscripts and t are adopted distinguish the source domain and the target domain.
4 Approach
Figure illustrates the overall structure of EDEN, consisting of a temporal feature extractor 4/r, frequency feature extractor vip, domain discriminator gD. a classifier g. and two auxiliary feature scorers Sy, Sy. Specifically, (1) To effectively utilize the multi-scale explicit domains, we propose Multi-Scale Curriculum Adaptation (MSCA) Section 4.1. (2) To fully exploit the multi-subspaae explicit domains, we propose Quality-Aware Feature Fusion (QFusion) in Section 4.2. (3) To utilize the multi-segment explicit domains, we propose Temporal Coherence Learning (TCL) in Section 4.3.
4.1 Multi-Scale Curriculum Adaptation
Intuitively, coarse-scale time series highlights macroscopic variations, i.e., low-frequency informa- tion, while deep neural networks demonstrate strong generalization capabilities for low-frequency information [39]. Fine-scale time series contain subtle changes local regions, i.e., high-frequency information, which may enhance feature discriminability but pose substantial challenges for domain

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(a) dA in T-subspace
(b) dA in F-subspace
(c) DANN in T-subspace (d) DANN in F-subspace
Figure 2: Fine-scale vs. Coarse-scale: Denote the original sampling rate as TO- Scale-M refers to
downsampling time series to a sampling rate of M- (a) A-distance in T-subspace. (b) A-distance in F-subspace. (c) DANN performance ratio of Scale-M to original data (Scale-1) in T-subspace.
(d) DANN performance ratio of Scale-M original data (Scale-1) in F-subspace. Reduced domain discrepancy may not guarantee accuracy gains due to the loss of discriminative information.
transfer. Transfer performance is jointly determined by transferability and discriminability. Therefore, multi-scale collaborative training may provide more performance gains compared to single-scale.
To validate the intuition, we investigate how representations with different scales influence the domain adaptation process. Based on the domain adaptation theory [2], the risk of the target domain can be bounded by the following proposition:
Proposition 4.1 (Domain Adaptation Bound). Let H be a hypothesis space, P. Q represent the source and larget domain respectively. For every h E H. the target risk eq (h) is bounded as:
(h(h)= S Er(h) + duHH(IN) +X,
(2)
2
where Cp(h) denotes the source risk, HAM-dissaace dHAH(P.Q) = Nu l<p(h, eq (h, 1!'l measures domuin shift as the discrepancy between the disugreement of two hypotheses h, it, and X = <p(h")+++ + eQ(h") is the error of the ideal joint hypothesis h²"
Under the supervision of source labels, p (h)) is usually smaller, while (h) is mainly determined by the latter two terms. We adopt the proxy of HAH--istaane A-distance [2], to quantify the domain discrepancy, defined as dA- 2(1 2e). where is the error rate of a domain classifier trained to discriminate source domain and target domain. As shown in Figure 2(a) and Figure 2(b), both in the temporal subspace and frequency subspace, it is consistently observed that the model trained on coarse-scale time series learns more commann-nvariial features with smaller A-distance. However, as scale M increases, the discriminative information gradually diminishes, potentially compromising prediction accuracy. In Figure 2(c) and Figure 2(d), we observe that reduced domain discrepancy may not guarantee accuracy gains with increasing M. This indicates that the coarse-scale features are easier for cross-domain transfer but potentially compromise discriminability. Consequently, better choice.
multi-scale collaborative training emerges
Inspired by curriculum learning [9, 29, 3]. we propose Multi-Scale Curriculum Adaptation to align the source and target distribution in an casy-to-hard way. In early training, coarse-scale features with stronger transferability guide the model to first stabilize global feature alignment. As the training progresses, fine-scale features gradually take the lead and refine local discriminating details.
Given multi-scale input X' (x', x", vi, v"), taking (x)xx") as the example, we extract fine-scale features and coarse-scale features respectively in the temporal subspace, i.c., E/r f7. = thr (x/xx) (similar to vV),,, and mix the two features in a curriculum manner as follows:
Fr Xfx + (1+ X)
X 1 - exp(-p) Ao
1+ exp(-p)
where À is progressively increased, is the ratio of the current number to the maximum number of iterations, and the X is the initial value of X. Denoting the mixing operation as (-).), we extract the temporal feature Fy- = hx (yT (x/, ))) and frequency feature fp = hy (Wr (v/, v*)), and then concatenate them to obtain the final representation f. The progress is formulated as follows:
(xx) [bx (VT (xl xx)) hrx (F' (v', vo)],.
(4)
5

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(a) T-subspace
(b) F-subspace
TF-subspace
(d) QFusion
Figure 3: Error matrices: (a) Temporal subspace. (b) Frequency subspace. (c) Temporal-frequency
subspace. (DTemmorralfrequenng subspace with Quusion.
where vb is denoted as the holistic extractor containing vht and thp. Based on the feature representation f = (x)).. we use the classifier 9 to make the prediction I (1b(()() and minimize Le with the labeled
source samples to guarantee lower source risk:
Lc X ".= CE (g (1b (Kr))) (x)),
(5)
where CE denotes cross-entropy loss. Meanwhile, we adopt the domain discriminator JD to align the source feature distribution and the target feature distribution via the minimax optimization problem:
D -IExx x; log [gD (v (22)))
Ex x log [ gn (vb (x
where Ln is minimized over gn but maximized over th.
4.2 Quality-Aware Feature Fusion
To explore the relationship between temporal subspace and frequency subspace, we delve into the error matrices of the target domain. We find that the classification performance of the same class may vary significantly between two subspaces. As shown in Figure 3(a) and Figure 3(b), the "sit" class is completely misclassified in the temporal subspace, while the class is classified correctly in the frequency subspace. Similarly, the "stairs up" class is classified correctly in the temporal subspace, but 21% % of the class is incorrectly classified "sits" in the frequency subspace. This suggests that the intrinsic characteristics a certain class can be more effectively captured the feature representation of one subspace compared the other. In other words, each subspace has its advantageous classes.
Ideally, a model trained jointly both subspaces, using the concatenation of temporal and frequency features final features, would achieve optimal performance in each subspace for every class, which can classify both the "sit" class and the "stairs up" class correctly. However, as shown in Figure 3(c), the model does not actively select the best performance in the two subspaces; instead, it only achieves a compromise between the temporal and frequency subspace performance, or even worse.
This prompts us to seek a criterion for measuring the quality of features across different subspaces to achieve optimal subspace fusion. As shown in Figure 1(c), we introduce Quality- -ware Feature Fusion, which contains feature scorers Sy, Sp into the temporal and frequency subspace respectively, responsible for measuring feature quality based on transferability, discriminability, and diversity, enabling adaptive temporal-frequenc integration. With ST and Sp, Equation (4) is modified to:
(xx) = (St (ir) Pr, Sr(() Tr].
Each score S(f) is comprised of three components: transferability criterion, discriminability criterion and diversity perturbation. The first two are key criteria to characterize the performance domain adaptation [8], while the latter ensures that different classes score diversely within the same subspace.
Transferability Criterion Transferability indicates the ability to learn domain- insiiinnivariann features. Inspired by A-distance [2], domain classifier employed to measure transferability. The objective of the domain classifier is to predict source samples as I and target samples as 0. The closer the prediction d is to 0.5, the more difficult it is to distinguish the feature across domains, indicating

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more domain invariant. Thus, can be used to quantify the transferability of each feature:
d Sigmoid (M) [0,1],
(8)
2/d 0.5| E [0, 1].
To train the domain classifier, 4 = CE(d, d) is calculated, where is the true domain label.
Discriminability Criterion Discriminability is the easiness of separating different categories by supervised classifier trained over the feature. Therefore, we use 1-layer MLP to classify the feature and the prediction y contains the discriminative information about the feature. The entropy and confidence of y are two uncertainty measurements on the discriminability. Smaller entropy, or higher confidence, means more discriminative features. Considering the complementarity of the two measurements [28], we use them simultaneously to quantify the discriminability of each feature:
y Softmax (MLP(F)),
Sent 1 1 (y) E [0, 1]. Scon = maxý E [o, 1],
where H() denotes the entropy, both Hy) and maxý are min-max normalize. To train the MIP, la CH(ý. y) is calculated on the source domain, where y is the ground-truth label of feature f.
Diversity Perturbation To enhance the diversity scores within subspace, we introduce diversity perturbation to facilitate an implicit competition between classes:
pert Sigmoid (MLP(f)) E [0,
(10)
la (Spert))
where C-))) denotes the calculation of the coefficient variation. The diversity perturbation encourages different classes to score diversely within the same subspace, especially promoting better scores for advantageous classes, thereby enhancing the discriminability the features.
The overall score S(f) is formalized follows:
S(f) Sd + Sent Sconf
1]8pert:
(11)
3
where y controls the perturbation intensity and is set to 0.1 in all experiments. The training loss of the scorer S is calculated as follows:
(12)
Given the heterogeneity of temporal and frequency subspace, use ST and SF measure their respective features independentl, rather than sharing the same scorer. Consequently, we have two training losses, εs, and Esp. The overall auxiliary loss is denoted as Ls LsT ESp.
4.3 Temporal Coherence Learning
During the continuous-time process Ps. participants were required to adhere specific activity protocol to ensure the validity of the data collection [1]. Therefore, given the raw series r,, its segments share similar class-related semantics, e.g., consistently running or standing state.
Based on the intrinsic coherence of time series, we propose Temporal Coherence Learning. Given X, that contain nearby segments exhibit temporal coherence, which can be reflected in logit-level and score-level. We achieve logit-level coherence by conditioning the logit distributions of X,,j and N₁ by minimizing the Kullback-Leible divergence follows:
X, {&,,) ) any X.J;
LTCL D (g( (1((()))))))))) ( (X),,))
(13)
To further complement the regularization, we impose score-level coherence learning and explicitly constrain the feature scores of QFusion assigned to nearby segments:
LTCL scare (fr,) (Trw) + (Sr(fr)) (r(rk))
(14)
Overall, temporal coherence learning guides the feature extractors, scorers and classifier to exhibit consistent and stable behavior, especially in the unsupervised target domain.
LTCL LreLscome score + LTCL-
(15)
7

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UCIHAR
HHAR-P
WISDM
FD
HHAR-D
EMG
Ours
5330
47.30
10.05
RAINCDAI
--44
15.75
2-1 03
CADAT'S
1641
CLUDA
-0.33
-03.3
-0.90
23.44
15.42
M|MM
2-4 74
AdaMstch
+00
CDAN
Source-dnly
100
50
70
90
60
80
80
100
40
60
80
80
90
Source-only only
General DA
Time Series DA
Figure 4: Average Accuracy (%) across multiple datasets.
Table 1: Accuracy (%)
EMG for unsupervised domain adaptation.
Method
0-+1
0-+2
0-+3
1 1-+2
1-+3
2-+0
2-+1
2-+3
3-+1
3-+2
Averuge
Source-only
84.94
74.38
73.38
74.38
73.88
73.88
82.16
73.69
79.38
72.31
76.24
CDAN
87.84
76.63
77.63
77.44
81.63
73.94
87.10
75.13
83.98
77.63
79.89
DeepCoral
87.50
76.44
76.19
77.63
77.63
74.69
84.72
75.50
81.93
74.88
78.71
AdaMatch
89.03
75.94
79.38
76.94
80.00
76.31
89.94
81.31
84.26
73.81
80.69
HoMM
87.61
76.50
75.75
77.00
77.94
73.94
84.89
75.88
82.61
75.31
78.74
DIRT-T
89.77
75.25
78.69
75.88
80.06
70.63
84.77
77.69
83.30
76.69
79.27
CLUDA
78.18
75.00
76.75
74.75
74.19
75.94
79.43
70.00
76.88
75.13
75.62
AdvSKM
86.42
75.94
76.25
77.25
78.00
74.88
85.06
77.25
81.76
75.31
78.81
CoDATS
88.24
77.44
78.31
78.44
81.81
73.75
86.65
78.88
84.43
78.06
80.60
RAINCOAT
89.60
77.00
78.56
78.25
83.13
73.06
85.68
76.88
83.13
74.00)
79.93
ACON
92.50
79.06
81.75
80.13
83.13
77.94
90.91
79.75
85.11
78.88
82.91
Ours
93.92
82.31
83.38
81.19
86.50
77.12
92.78
82.88
87.61
78.38
84.61
4.4 Overall Training Objective
During training, our method trains the temporal feature extractor vh., frequency feature extractor oh and classifier by minimizing the supervised classification loss Lo on the labeled source domain. To learn Nomaan--nvvrian features,
method adopts the adversarial training facilitated with Gradient Reversal Layer between thy, the and the domain discriminator 9D by minimizing the adversarial loss Cn. Simultaneously. we train the feature scorers Sy and Sp evaluate feature quality through the auxiliary loss Ls. Furthermore, the coherence loss LrCI. is minimized to preserve temporal coherence of vhr, Vr, Sr, Sp and g. The overall training objectives is formulated
L Le + LD Lss 3LTC.
(16)
Experiments
5.1 Setup
Datasets We conduct extensive experiments using six benchmark datasets: (1) UCIHAR [1],
HHAR-P [32, 27], WISDM[16] and HHAR-D [32, 11] for sensor-based human activity recognition. (2) FD [27] for machine fault diagnosis. (3) EMG [20, 23] for gesture recognition. For each dataset, following the existing DA methods on time series [4. 14, 18], we sample 10 source-target domain pairs for evaluation. Further details, processing and domain splits are included in Appendix A.
Baselines (1) We report the performance of the model without UDA (Source-only) in the temporal domain to show the overall contribution of UDA methods.
(2) We implement the following state- of-the-art baselines for TSDA: CODATS [38], AdvSKM [19], CLUDA [25], RAINCOAT [14] and ACON [18]. (3) We additionally implement general UDA methods: CDAN [22], DeepCoral [33], AdaMatch [4], HoMM [6] and DIRT-T [31]. For each baseline, we ensure fair implementations by
maintaining identical backbones, frameworks, and hyperparameter settings from prior works.
Implementation We adopt the implementation of AdaTime [27] as the benchmarking suites for domain adaptation on time series data. We use ID-CNN as temporal feature extractor base and complex- valued lincar frequency feature extractor base. We report accuracy and Macro-Fl Score

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calculated using target test set as evaluation metries. Each experiment is repeated 5 times with different random seeds. Detailed model architectures and optimal hyperparameters are included in Appendix B. Computational cost is included in Appendix C.I.
5.2 Results
Figure 4 shows the average accuracy and error bars of each method for 10 source-target domain pairs on the datasets. Overall, EDEN consistently improves the performance the best baseline by large margin. It's worth noting that EDEN also reduces the error bars, demonstrating the reliability. EDEN has won 6 out of tests and makes an average improvement of 4.80% for the accuracy metrie. Specifically, EDEN improves prediction accuracy by 6.87% on the HHAR-P dataset, 6.88% on the FD dataset, and 8.61% % on the HHAR-D dataset over the advanced baseline on each dataset respectively. Due to the limited pages, we report the results for selected source-targe domain pairs with metric accuracy on the EMG dataset. More accuracy results are given in Table 6-10. Average macro-fl score is provided in Figure 8 and full macro-fl score results are given in Table 12-17.
5.3 Analysis
Module Ablation We conduct a comprehensive ablation study in Table 2. We investigate the effec- tiveness of three modules EDEN and analyze the interaction between them. EDEN's three modules show performance gains when added individually (Rows 2-4) and demonstrate mutual promotion in pairwise combinations (Rows 5-7). Full integration (Row achieves optimal performance.
Table 2: Ablation of EDEN, MSCA and QFusion.
MSCA Ablation We verify the effective-
Module Ablation
ness of MSCA in the different subspaces. average improvement of 2.61% % in the temporal subspace (as shown
MSCA
QFosion
TCL
EDEN
HHHAR-P
Average
MSCA yields
97.16
83.47
83.58
88.07
97.48
65.92
$3.79
$9.06
in Rows 9-11 of Table 2). and an average improvement of 4.08% in the frequency subspace (Rows 12-14). By integrating the temporal subspace and the frequency sub- space (Row 15), MSCA achieves the best performance, outperforming the single- subspace MSCA variants by 3.68%.
/
97.65
R5.75
85.53
89.64
97.77
86.12
85.43
89.77
97.88
85.97
74
98.10
87.36
87.58
91.01
MSCA Ablation
Co
HIARR-P
Average
95.13
76.72
76.48
82.78
96.17
77.55
$1.10
84.94
66.88
$1.08
56.47
58.14
QFusion Ablation We investigate the ef-
V
94.50
75.93
73.03
$1.15
fectiveness of three components in QFu- sion score. Rows 17-18 Table ap- ply transferability and discriminability cri- terion respectively, both showing perfor- mance gains. In Row 19, by combin- ing transfcrability, discriminability, and di- versity, QFusion achieves the best perfor- mance, demonstrating synergistic effect.
76.05
97.16
83,47
83,58
$8.07
QFusson Ablatio
Son!
Sport
UCHHAR
HIHAR-P
WISDM
Average
82.68
86.50
97.00
83.05
82.77
87.61
97.42
84.32
$2.94
S8.23
97.48
85.92
83.79
89.06
Sensitivity Analysis the parameter Åo are included in Appendix C.2. As shown in Figure 5-7, EDEN exhibits stable performance across reasonable variations and consistently outperforms baselines on multiple datasets.
The sensitivity analysis on the coarser scale M, the trade-off 3 of LICL and
6 Conclusion
In this paper, we propose EDEN, novel framework for TSDA based on multiple explicit domains, first breaking through the limitations of the sensor-limited single view in the original datasets, taking advantage of the comprehensive reflection of implicit temporal dynamics. Specifically, Multi-Scale Curriculum Adaptation is proposed to align the source and the target domain; Quality-Aware Feature Fusion is proposed to facilitate adaptive integration of temporal and frequency features; Temporal Coherence Learning is proposed to encourage the model to exhibit consistent and stable behavior. Notably, EDEN yields significant performance improvements a wide range of time series datasets.

---

Acknowledgements
This work was supported by the National Natural Science Foundation China (62306085, 62206074 62476071, U23B2055, U24A20328), Shenzhen College Stability Support Plan (GXWD202311331113229022 GXWD202008113329000 Fund (CCF-ALIMAMA OF 2025001), Guangdong Basic and Applied Basic Research Foun- dation (2025A1515012933, 2025A151501 1732),,,, Shenzhen Science and Technology Program IKQTDD0024022110215466 ZDSSS20233022609000000000
CCF-ALIMAMA TECH Kangaroo
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A Datasets
A.1 Detailed Statisties
We conduct extensive experiments using a wide range of time series datasets. The detailed statistics for each dataset is included in Table For UCIHAR, HHAR-P, WISDM and FD datasets, we use the processed versions released by AdaTime [27]. For HHAR-D datasets, we use the processed versions released by WOODS [11]. For EMG dataset, we use the processed version released by DIVERSIFY [24].
Table 3: Summary of datasets.
Datasct
Subjects
Channels
Length
Class
Total
UCIHAR
30
9
128
6
3290
HHAR-P
3
128
17934
WISDM
128
2070
HHAR-D
6
500
13674
EMG
4
8
200
6
6883
FD
4
1
5120
10916
A.2
Dataset Processing
Each domain of datasets is randomly divided into 80% training, and 20% testing. We follow Adatime [27]. apply Z-score normalization to both the training and testing splits of the data, using the following equation:
normmaliz 23
meaa
= 1,2,
(17)
pstdd
where N₂ for the source domain data and N N, for the target domain data. Note that both the training and testing splits are normalized based on the training set statistics only.
B Experimental Details
B.1 Model Architecture
Different from existing methods, our method simultaneously captures both coarse-scale and fine-scale feature representations by tailored feature extractors. Coarse-scale and fine-scale data typically exhibit varying lengths and contain distinct patterns, making it difficult to extract features simultaneously using a single CNN or MLP. Recent foundational models tailored for multi-scale time series analysis generally employ separate feed-forward layers per scale followed by feature aggregation [36, 35]. Aligned with the TSDA benchmark 's architectures [27, 18] that are based on ID-CNN in temporal subspace and MLP in frequency subspace, incorporate a smaller CNN and MLP. In this way, we use a larger feature extractor to capture fine- scale features and a smaller feature extractor obtain coarse-scale features. This configuration enables dedicated extraction of both fine-scale and coarse-scale characteristics.
follow the structure and additionally
Table 4: Key hyperparameters for EDEN.
Hyperparameter
UCIHAR
HHAR-P
WISDM
HHAR-D
EMG
FD
Epoch Batch Size
50
50
50
50
50
32
32
32
32
32
32
Coarse Scale
2
4
4
4
4
Learning Rate
0.01
0.001
0.003
0.001
0.001
0.01
B.2 Multi-scale explicit domains
As mentioned in Section 4.1, limiting the input to a single scale makes determining the optimal scale for each dataset time-consuming. Therefore, we model both fine-scale and coarse-scale features simultaneously, leveraging the advantages of each. The fine-scale data refers to the raw data. Coarse- scale data obtained by downsampling fine-scale data at scale M. The coarse scales M chosen for different datasets are listed in Table 4. Sensitivity analysis of scale M is presented in Figure 5.
13

---

The proposed MSCA consistently achieves performance improvement's across viable scales M in {2, 4, 6, 83. demonstrating the robustness of scale selection.
B.3 Multi-segment explicit domains
We introduce our segment strategy in two phases: training and testing. For the training phase, given raw data E RCT,, we randomly crop two short segments from the original dataset using window size of T/2, while retaining the original segment. These segments form multi-segment explicit domains and are constrained for coherence through Temporal Coherence Learning. For the testing phase, divide the original data into two halves using the same window size of T/2 to ensure consistency in each test. We predict for both segments separately, and the ensemble of these predictions is recorded as the final prediction result.
C
Further Analysis
C.1
Computational cost
Table 5: Training time and model performance on the FD dataset.
FD
CDAN
Raincoat
ACON
EDEN
Accuracy
90.56
86.75
91.74
98.05
Training Time
1.08h
2.55h
1.25h
1.38h
Raincoat, ACON, and EDEN vs. CDAN: Due to additional cues like time-frequency transformation, methods tailored for time series achieve performance improvements while increasing training time.
EDEN vs. Raincoat: EDEN not only significantly outperforms Raincoat but also reduces the training time. Raincoat has a longer training time due to its reconstruction-coreedd
mechanism.
EDEN ACON: Due the introduction of multi-scale and multi-segment explicit domains, the training time of EDEN is slightly longer than ACON, However, considering the significant performance gains EDEN (6.88%), the total training time is entirely acceptable.
C.2 Sensitivity Analysis
Sensitivity Analysis of Coarse Scale M As shown in Figure 5, our method exhibits stable perfor- mance on the multiple datasets across reasonable variations, with optimal performance at scales of or 4. The optimal scale of different datasets is included in Table 4.
Sensitivity Analysis of TCL trade-off 3 We investigate EDEN's sensitivity to the hyperparameter in Equation (16). As shown in Figure 6, EDEN remains stable within 0.2 1.4, with the optimal average performance at the value of Therefore, 3 is fixed at 1 in all our experiments.
Sensitivity Analysis of λσ The hyperparameter Åo determines the dominant strength of coarse-scale at the initial training. Coarse-scale features dominate early alignment, with the weight decreasing from + An - Ao while fine-scale features progressively intensify, with the weight increasing from An - + Åg In all our experiments, An is fixed at 0.5. As shown in Figure 7, EDEN exhibits smooth variations, demonstrating that our Multi-Scale Curriculum Adaptation achieves collaborative training of multi-scale explicit domains, consistently outperforming single-scale.
Sessitiity Scile
Sensiivity
Sensbivity analyaaa
Senabiity Scac
(a) UCIHAR
(b) HHAR-P
(c) WISDM
(d) FD
Figure 5: Sensitivity Analysis of Coarse Scale M.
14

---

(a) UCIHAR
(b) HHAR-P
(c) WISDM
(d) FD
Figure 6: Sensitivity Analysis of TCL trade-off B.
(a) UCIHAR
(b) HHAR-P
(c) WISDM
(d) FD
Figure Sensitivity Analysis of Xg-
D Broader Impacts
We investigate the unsupervised domain adaptation method for time series classitication by exploring multiple explicit domains. This paper aims to advance the field of time series analysis and the real-world deployment of time series applications without any negative social impact.
E
Full Results
UCIHAR
HHAR-P
WISDM
FD
HHAR-D
EMG
Ours
RAINCOAT
0124
80
CaDAT'S
155
3.45
6.79
CLUDA
HMMM
579
1.4
2.43
6,53
2.40
6.79
AdaMatch
6.79
O3AN
only
0.8 1.0
1.0
0.6
0.8
0.8
0.9
Source-only
General
Time Series DA
Ours
Figure 8: Average Macro-F1 Score across multiple datasets.
15

---

Table 6: Accuracy (%) on UCIHAR for unsupervised domain adaptation.
Method
2-+11
6-+23
7-+13
9-+18
12-+16
13-+19
18-+21
20-+6
23-+13
24-+12
Averuge
Source-only
76.56
67.36
83.68
24.65
61.11
88.89
100.0
94.10
71.18
83.68
75.12
CDAN
85.42
87.50
92.01
58.86 86
66.67
96.52
100.0
95.13
82.64
93.40
85.78
DeepCoral
90.63
84.38
87.50
46.88
65.28
95.49
100.0
95.49
69.79
87.50
82.01
AdaMatch
75.00
80.20
85.76
56.59
49.65
94.79
100.0
84.37
68.75
70.83
76.07
HoMM
74.06
82.71
81.88
73,96
70.21
96.67
98.75
73.33
77.71
80.63
80.99
DIRT-T
80.21
74.31
82.99
59.03
67.01
99.30
98.61
92.36
74.72
94.27
83.26
CLUDA
81.77
92.01
99.31
67.71
65.28
94.44
98.96
97.22
72.92
99.31
85,53
AdvSKM
98.96
88.54
92.71
74.65
69.44
93.05
100.0
85,41
79.51
96.87
83.26
CoDATS
68.23
74.31
77.43
63.89
66.32
94.09
99.65
70.49
56.25
82.81
75.54
RAINCOAT
100.0
95.83
100.0
75.69
86.52
100.0
100.0
93.41
86.52
93.75
94.43
ACON
100.0
96.25
99.16
91.66
85.63
100.0
100.0
97.50
100..0
100.0
97.02
Ours
100.0
99.17
98.96
93.12
90.62
100.0
100.0
99.17
100.0
100.0
98.10
Table 7: Accuracy (%) on HHAR-P P for unsupervised domain adaptation.
Method
0-+2
1-+6
2-+4
4-+
4-+5
5-+1
5-+2
7-+2
7-+5
8-+4
Average
Source-only
64.51
70.63
45.42
32.81
78.32
90.63
25.67
32.37
39.26
62.92
54.25
CDAN
76.19
92.57
52.57
29.09
97.27
16.16
35.04
37.05
75.26
96.
68.73
DeepCoral
84.23
90.14
47.08
28.13
90.49
89.91
38.39
34.45
55.73
76.88
68.03
AdaMatch
84.78
92.31
54.50
36.45
78.45
94.20
41.96
37.65
63.80
64.69
65.91
HoMM
75.67
90.79
52.83
36.61
87.66
90.78
37.23
37.32
61.29
79.88
65.01
DIRT-T
77.83
88.54
50.69
32.22
93.16
91.86
38.62
38.10
72.46
65.83
64.99
CLUDA
79.84
93,40
45.90
38.84
94.08
95.57
33,93
37.80
77.57
96.52
69.35
AdvSKM
78.94
87.91
52.57
33.49
92.64
92.71
36.53
39.95
65.49
83.75
66.41
CoDATS
79.61
90.90
60.07
21.80
97.66
97.66
41.44
38.54
58.15
97.01
68.71
RAINCOAT
87.72
93.33
63.75
46.46
98.05
98.25
42.63
43.32
84.17
93.75
74.21
ACON
86.65
93.45
79.01
53.53
97.15
98.32
65.80
65.71
88.59
89.17
81.74
Ours
89.64
95.17
93.75
76.61
98.40
98.59
59.69
67.95
95.35
98.50
87.36
Table 8: Accuracy (%) on WISDM for unsupervised domain adaptation.
Method
2-+32
4-+15
7-+30
12-+7
12-+19
18-+20
20-+30
21 ++31
25
26-+2
Average
Source-only
81.16
79.86
89.32
71.53
54.29
83.74
67.96
21.29
26.11
82.52
65.78
CDAN
89.37
65.97
84.79
70.48
51.01
88.62
77.02
46.58
44.33
83.33
70,05
DeepCoral
87.92
62.50
91.26
79.86
51.77
64.23
81.88
54.62
53.89
77.44
70.80
AdaMatch
74.39
78.47
89.64
73.26
55.30
75.20
74.76
31.32
57.78
87.20
69.79
HoMM
77.10
74.58
78.64
68.13
50.61
71.22
72.82
56.39
57.00
66.10
67.26
DIRT-T
77.78
70.83
90.61
70.20
51.51
85.36
71.84
54.41
60.04
66.46
69.62
CLUDA
73.91
67.36
86.40
65.97
49.24
83.74
72.49
49.97
35.00
86.47
67.04
AdvSKM
70.83
95.85
93.85
77.08
47.47
81.30
21.28
44.45
74.79
74.95
66.97
CoDATS
77.29
70.83
83,20
70.17
47.47
76.01
82.85
52.61
53.89
83.29
70.66
RAINCOAT
79.71
97.91
91.28
89.80
85.00
92.23
91.66
59.09
82.97
83.50
76.60
ACON
89.86
86.25
98.06
98.13
77.73
83.66
91.26
63.61
60.00)
99.51
84.80
Ours
92.50
90.62
97.29
95.42
82.97
88.44
90.42
76.56
65.00
96.56
87.58
Table 9: Accuracy (%) on FD for unsupervised domain adaptation.
Method
0-+1
0-+2
0-+3
1-+0
1-+2
2-+0
2-+1
2-+3
3-+0
3-+2
Average
Source-only
62.21
53.71
62.41
63.91
73.95
64.08
93.17
95.54
57.08
74.31
70.04
CDAN
91.29
71.83
90.13
96.50
90.09
83.10
99.38
99.98
95.40
87.95
90.56
DeepCoral
75.54
71.79
76.03
89.13
83.55
76.34
98.84
98.55
87.50
83.71
84.10
AdaMatch
67.81
55.38
62.88
92.21
98.57
79.08
89.96
90.40
87.23
97.57
82.11
HoMM
81.54
71.63
78.17
89.89
84.78
76.03
98.71
99.55
90.94
85.96
85.72
DIRT-T
75.94
70.85
76.36
98. 10
90.27
81.92
100.0
99.98
97.06
90.29
88.08
CLUDA
90.47
82.63
88.68
89.06
92.23
61.92
93.91
90.80
82.01
78.17
84.99
AdvSKM
74.71
66.05
73.30
87.86
86.29
76.85
98.66
99.38
84.89
85.74
83.37
CoDATS
81.79
73.26
83.15
89,22
88.68
81 1..4
99.89
100.0
85.47
89.00
87.20
RAINCOAT
85.18
79.40
89.04
78.84
90.11
81.43
95.18
96,81
77.39
94.08
86.75
ACON
86.52
69.00
86.96
97.92
99.80
84.29
98.62
98.93
97.72
97.66
91.74
Ours
98.48
97.39
98.71
98.37
100.0
89.22
100.0
100.0
98.37
100.0
98.05
16

---

Table 10: Accuracy (%)
HHAR-D for unsupervised domain adaptation.
Method
0-+1
0-+2
0-+3
0-+4
1-+0
1-+3
1-+4
2-+1
3-24
4-+1
Average
Source-only
65,48
33.59
31.71
39.79
34.69
44.83
49.54
38.17
86.17
44.23
46.82
CDAN
69.86
48,28
38.22
48.42
48.75
60.48
51.33
47.84
87.33
48.89
54.94
DeepCoral
68.94
42.88
40.67
47.96
35.63
55.31
56.21
44.71
87.25
45.96
52.55
AdaMatch
71.78
39.60
39.74
47.50
52.50
55,48
58.33
46.49
85.83
41.15
53.84
HoMM
69.66
40.51
39.16
50.42
35.94
55.02
57.13
42.36
86.79
46.35
52.33
DIRFT
68.37
42.14
47.21
52.92
41.25
60.14
55.63
46.73
92.25
54.81
56.14
CLUDA
71.78
39.60
39.74
47.50
52.50
55.48
58.33
46.49
85.83
41.15
53.84
AdvSKM
67.93
40.71
40.19
47.33
37.19
55.65
59.54
42.69
87.46
49.33
52.80
CoDATS
72.50
43.35
50.79
45.50
58.44
62.24
54.54
40.14
89.63
45.53
56.27
RAINCOAT
74.47
36.52
48.82
35.29
51.25
41.49
41.50
34.28
88.58
38.46
49.07
ACON
77.50
61.36
54.69
65.46
69.38
71,30
62.13
50. 10
93.63
44.86
65.04
Ours
75.67
54.44
51.11
61.04
79.38
89,59
76.38
58.75
94.88
65.19
70.64
Table 11: Accuracy (%) on EMG for unsupervised domain adaptation.
Method
0-+1
0-+2
0-+3
1 1-+2
1-+3
2-+0
2-+1
2-+3
3-+1
3-42
Average
Source-only
84.94
74.38
73.38
74,38
73.88
73.88
82.16 16
73.69
79.38
72.31
76.24
CDAN
87.84
76.63
77.63
77.44
81.63
73.94
87.10
75.13
83.98
77.63
79.89
DeepCoral
87.50
76.44
76.19
77.63
77.63
74.69
84.72
75.50
81.93
74.88
78.71
AdaMatch
89.03
75.94
79.38
76.94
80.00
76.31
89.94
81.31
84.26
73.81
80.69
HoMM
87.61
76.50
75.75
77.00
77.94
73.94
84.89
75.88
82.61
75.31
78.74
DIRFT
89.77
75.25
78.69
75.88
80.06
70.63
84.77
77.69
83.30
76.69
79.27
CLUDA
78.18
75.00
76.75
74.75
74.19
75.94
79.43
70.00
76.88
75.13
75.62
AdvSKM
86.42
75.94
76.25
77.25
78.00
74.88
77.25
81.76
75.31
78.81
CoDATS
88.24
77.44
78.31
78.44
81.81
73.75
86.65
78.88
84.43
78.06
80.66
RAINCOAT
89.60
77.00
78.56
78.25
83.13
73.06
85.68
76.88
83.13
74.00
79.93
ACON
92.50
79.06
81.75
80.
83.13
77.94
90.91
79.75
85.11
78.88
82.91
Ours
93.92
82.31
83.38
81.19
86.50
77.12
92.78
82.88
87.61
78.38
84.61
Table 12:
Macro-F1 Score on UCIHAR for unsupervised domain adaptation.
Method
2-+11
6-+23
7-+13
9-+18
12-+16
13-+19
18-+21
20-+6
23
24-+12
Average
Source-only
0.69
0.63
0.84
0.17
0.58
0.91
1.00
0.94
0.71
0.84
0.73
CDAN
0.85
0.88
0.91
0.61
0.64
0.97
1.00
0.95
0.82
0.92
0.86
DeepCoral
0.91
0.81
0.87
0.44
0.65
0.95
1.00)
0.95
0.70
0.88
0.82
AdaMatch
0.73
0.81
0.86
0.55
0.48
0.94
1.00
0.84
0.67
0.70
0.76
HoMM
0.73
0.78
0.81
0.69
0.69
0.96
0.99
0.71
0.75
0.78
0.79
DIRT-T
0.81
0.68
0.82
0.58
0.62
0.99
0.98
0.92
0.74
0.93
0.81
CLUDA
0.81
0.92
0.99
0.67
0.64
0.94
0.99
0.98
0.71
0.99
0.86
AdvSKM
0.99
0.87
0.73
0.68
0.93
0.84
0.77
0.87
CoDATS
0.66
0.71
0.78
0.60
0.64
0.93
0.99
0.65
0.54
0.81
0.72
RAINCOAT
1.00
0.96
1.00
0.76
0.86
1.00
1.00
0.94
0.86
0.94
0.93
ACON
1.00
0.97
0.99
0.91
0.86
1.00
1.00
0.98
1.00
1.00
0.97
Ours
1.00
0.99
0.99
0.94
0.92
1.00
1.00
0.99
1.00
1.00
0.98
Table 13:
Macro-FF F1 Score on HHAR-P for unsupervised domain adaptation.
Method
0-22
1->6
2-14
4-+0
4-+5
5-+1
5-+2
7-+2
7-:5
8-
Average
Source-only
0.60
0.64
0.32
0.29
0.78
0.90
0.19
0.31
0.36
0.58
0.50
CDAN
0.70
0.93
0.52
0.27
0.98
0.98
0.35
0.32
0.76
0.97
0.68
DeepCoral
0.86
0.91
0.45
0.26
0.90
0.90
0.36
0.32
0.50
0.73
0.62
AdaMatch
0.83
0.93
0.46
0.32
0.76
0.94
0.40
0.37
0.60
0.61
0.62
HoMM
0.70
0.91
0.45
0.37
0.88
0.91
0.34
0.40
0.61
0.79
0.64
DIRT-T
0.76
0.86
0.51
0.30
0.93
0.90
0.36
0.34
0.73
0.64
0.64
CLUDA
0.82
0.94
0.44
0.40
0.94
0.96
0.37
0.36
0.65
0.84
0.67
AdvSKM
0.72
0.88
0.93
0.92
0.41
0.64
0.83
CoDATS
0.73
0.90
0.20
0.96
0.94
0.41
0.36
0.59
0.95
RAINCOAT
0.87
0.93
0.59
0.45
0.98
0.98
0.41
0.44
0.86
0.94
0.75
ACON
0.86
0.93
0.74
0.52
0.97
0.98
0.62
0.65
0.89
0.89
0.80
Ours
0.88
0.95
0.94
0.76
0.98
0.99
0.59
0.67
0.95
0.98
0.87

---

Table 14: Macro-FI Score on WISDM for unsupervised domain adaptation.
Method
2-+32
4-+15
7-+30
12-+7
12-+19
18-+20
20-+30
21 21-+31
25-+29
26-+2
Average
Source-only
0.68
0.52
0.77
0.53
0.36
0.81
0.56
0.10
0.15
0.69
0.52
CDAN
0.72
0.44
0.70
0.50
0.31
0.87
0.64
0.31
0.23
0.71
0.54
DeepCoral
0.71
0.42
0.85
0.67
0.35
0.63
0.67
0.27
0.25
0.64
0.52
AdaMatch
0.59
0.54
0.76
0.67
0.38
0.66
0.54
0.16
0.24
0.74
0.54
HoMM
0.63
0.42
0.62
0.55
0.39
0.63
0.60
0.30
0.26
0.54
0.49
DIRFT
0.65
0.41
0.78
0.56
0.39
0.67
0.65
0.28
0.21
0.54
0.54
CLUDA
0.64
0.61
0.81
0.59
0.41
0.70
0.70
0.27
0.26
0.75
0.57
AdvSKM
0.61
0.55
0.35
0.71
0.28
0.28
0.55
CoDATS
0.66
0.41
0.75
0.62
0.37
0.76
0.72
0.30
0.30
RAINCOAT
0.68
0.98
0.86
0.72
0.78
0.92
0.87
0.43
0.44
0.75
0.74
ACON
0.81
0.65
0.99
1.00
0.63
0.76
0.87
0.36
0.28
1.00
0.74
Ours
0.88
0.76
0.96
0.91
0.68
0.85
0.87
0.42
0.31
0.94
0.76
Table 15: Macro-PF Score on FD for unsupervised domain adaptation.
Method
0-+1
0-+2
0-+3
1-+0
1-+2
2-+0
2-+1
2-+3
3-+0
3-42
Average
Source-only
0.41
0.33
0.41
0.65
0.77
0.64
0.95
0.97
0.59
0.78
0.65
CDAN
0.91
0.76
0.90
0.95
0.92
0.86
1.00
1.00
0.94
0.91
0.92
DeepCoral
0.61
0.62
0.62
0.90
0.87
0.77
0.99
0.99
0.89
0.88
0.81
AdaMatch
0.50
0.45
0.46
0.91
0.98
0.80
0.93
0.93
0.87
0.97
0.78
HoMM
0.61
0.52
0.62
0.91
0.88
0.78
0.99
1.00
0.91
0.89
0.81
DIRFT
0.80
0.62
0.70
0.97
0.93
0.84
1.00
1.00
0.96
0.93
0.88
CLUDA
0.84
0.80
0.79
0.88
0.93
0.50
0,95
0,90
0.84
0.80
0.82
AdvSKM
0.55
0.54
0.89
0.76
1.00
0.87
0.89
CoDATS
0.80
0.69
0.87
0.90
0.92
0.86
1.00)
1.00
0.87
0.92
RAINCOAT
0.89
0.84
0.92
0.81
0.92
0.85
0.96
0.98
0.81
0.94
0.89
ACON
0.86
0.75
0.89
0.96
1.00
0.88
0.99
0.99
0.96
0.98
0.93
Ours
0.98
0.98
0.99
0.97
1.00
0.92
1.00
1.00
0.97
1.00
0.98
Table 16: Macro-F1 Score on HHAR-D for unsupervised domain adaptation.
Method
0-+1
0-+2
0-+3
0-+4
1-+0
1-+3
1-+4
2-+1
3-44
4
Average
Source-only
0.61
0.27
0.25
0.33
0.44
0.43
0.46
0.32
0.85
0.38
0.43
CDAN
0.67
0.42
0.35
0.42
0.66
0.57
0.50
0.44
0.88
0.44
0.53
DeepCoral
0.65
0.34
0.33
0.40
0)448
0.53
0.53
0.39
0.86
0.41
0.49
AdaMatch
0.69
0.36
0.36
0.41
0.660
0.49
0.56
0.41
0.86
0.36
0.51
HoMM
0.66
0.33
0.31
0.41
0.47
0.52
0.53
0.37
0.86
0.42
0.49
DIRT-T
0.66
0.38
0.40
0.44
0.52
0.60
0.53
0.39
0.93
0.49
0.53
CLUDA
0.69
0.36
0.36
0.41
0.60
0).99
0.56
0.41
0.86
0.36
0.51
AdvSKM
0.63
0.32
0.31
0.38
0.46
0.54
0.36
0.86
CoDATS
0.71
0.38
0.44
0.39
0.70
0.61
0.53
0.38
0.90
0.55
RAINCOAT
0.72
0.32
0.42
0.32
0.56
0.39
0.38
0.31
0.89
0.35
0.47
ACON
0.76
0.53
0.49
0.56
0.81
0.67
0.59
0.44
0.93
0.41
0.62
Ours
0.73
0.49
0.44
0.54
0.82
0.89
0.76
0.55
0.95
0.61
0.68
Table 17: Macro-Fl Score on EMG for unsupervised domain adaptation.
Method
0-1
0-+2
0-+3
1-+2
1 1-+3
2-+0
2-+1
2-+3
3-+1
3-+2
Average
Source-only
0.85
0.74
0.74
0.74
0.75
0.75
0.82
0.74
0.78
0.72
0.76
CDAN
0.88
0.77
0.78
0.78
0.82
0.74
0.87
0.76
0.84
0.78
0.80
DeepCoral
0.87
0.76
0.76
0.78
0.78
0.75
0.84
0.76
0.82
0.75
0.79
AdaMatch
0.89
0.76
0.79
0.77
0.80
0.76
0.90
0.81
0.84
0.74
0.81
HoMM
0.87
0.77
0.76
0.77
0.78
0.74
0.84
0.76
0.82
0.75
0.79
DIRT-T
0.90
0.75
0.79
0.76
0.80
0.71
0.84
0.78
0.83
0.77
0.79
CLUDA
0.78
0.75
0.75
0.74
0.76
0.79
0.70
0.75
0.75
0.75
AdvSKM
0.86
0.76
0.77
0.78
0.76
0.77
0.81
CoDATS
0.88
0.77
0.78
0.79
0.82
0.74
0.79
0.84
0.78
RAINCOAT
0.89
0.77
0.79
0.78
0.83
0.73
0.85
0.77
0.83
0.74
0.80
ACON
0.92
0.79
0.82
0.80
0.83
0.78
0.91
0.80
0.85
0.79
0.83
Ours
0.94
0.82
0.83
0.81
0.87
0.77
0.93
0.83
0.87
0.79
0.85
18

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Reviews:
[{"review_id": "l5Xn0ORhlx", "reviewer": "Reviewer_KFEc", "summary": "This 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.", "questions": "1）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", "limitations": "yes", "rating": 4, "confidence": 1, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your helpful feedback! We are grateful that the reviewer highlights our motivations and experimental results. \n\n### **W1: The abstract and introduction lack clarity in conveying the core ideas behind \"multi-scale\" and \"multi-subspace\" modeling.**\n\nThank you for your valuable feedback. \n\nWe will revise the abstract and introduction to improve clarity and add explanations: \n\n- \"Multi-Scale Curriculum Adaptation progressively integrates coarse and fine-scale features to obtain the final extracted features, facilitating curriculum adaptation from coarse-to-fine by aligning the mixed feature between source and target domains. \"\n- \"Quality-Aware Feature Fusion measures feature quality through transferability and discriminative scores for adaptive fusion. The transferability criterion is determined by A-distance, which reflects the prediction confidence of the domain classifier, while the discriminative criterion is based on the entropy and confidence of the classifier. To enhance score diversity at the class level, we also introduce diversity perturbation by encouraging the coefficient of variation.\"\n\n### **Q1: The assumption that coarse-scale features are easier and more domain-invariant needs clearer theoretical or empirical support.**\n\nEmpirically, in **Figure 2 (a)(b)** of our paper, we **use A-distance [1] to quantify domain discrepancy** of features at different scales. We further report the A-distance on three additional datasets in the following table. Combined with Figure 2(a)(b), the results validate our assumption on all six datasets, demonstrating that aligned features of coarse-scale time series exhibit smaller domain discrepancies. This quantitative analysis adequately supports the proposed coarse-to-fine curriculum.\n\nTheoretically, as noted in Proposition 4.1 of our paper, A-distance $d_A = 2(1-2\\epsilon)$is determined by the generalization error $\\epsilon\n$ of discriminating between source and target examples, and serves as a proxy of $\\mathcal{H}$-divergence [2] between the source domain $P$ and target domain $Q$ , $d_\\mathcal{H}(P, Q)=2\\sup_{h\\in\\mathcal{H}}|Pr_P[h]-Pr_Q[h]|$​. This metric guides the family of domain adversarial learning methods (e.g., DANN, CDAN), providing valid estimates of domain discrepancy.\n\n| A-distance | FD   | HHAR-D | EMG  |\n| :--------- | :--- | :----- | :--- |\n| Scale 1    | 0.18 | 0.82   | 0.36 |\n| Scale 2    | 0.13 | 0.80   | 0.35 |\n| Scale 4    | 0.11 | 0.68   | 0.31 |\n\n[1] Domain-Adversarial Training of Neural Networks. *JMLR*, 2016.\n\n[2] A theory of learning from different domains. *Machine learning*, 2010.\n\n### **Q2: Handle different time scales of source and target data**\n\nIn existing time series analysis work, it is generally assumed that **all training and testing data for the same feature extractor should have the same resolution**. When there is a significant difference in resolution among data, it is typically addressed by ensuring consistency through downsampling. This is because time series data has a high information density; when the sampling rates differ, a segment of length 5 may capture variations within 5 hours in one case and variations over 5 days in another, leading to completely different distributions. Therefore, it is not feasible to use the same feature extractor for both.\n\n### **Q3: Only three types of explicit domains are explored. other forms—e.g., modality, condition, or context—be useful?**\n\nThank you for your valuable insights. Leveraging other forms for domain adaptation is a promising research direction. \nHowever, it is important to note that the other forms, e.g., modality, condition, or context, typically **depend on external knowledge**, which is not universally available across datasets. \n\nIn contrast, our proposed three explicit domains are derived from the original time series **without relying on any external information**, and are universal across multiple datasets. These explicit domains are the only three explicit domains, which is fundamentally determined by the intrinsic limitations of time series sampling (sampling rate, record duration) and the inherent property that discrete signals can be transformed into the frequency domain.\n\nWe believe that external modalities, conditions, or contexts can enhance UDA performance from two aspects. \n\n- Meta-information guided transfer\n  For example, in action recognition tasks, we know the context of the subjects' dominant hands—one being left-handed and the other right-handed. Using this information, we can adjust the distribution of channels in the original series to achieve spatial alignment at the channel level.\n- External conditions assisted classification\n  For example, in action recognition tasks, the data not only includes time series but also contains determining conditions. For instance, if the main frequency is below a specified value, it indicates a stationary state. We can use this information to assist in classification.\n\n### **Q4: Module Naming Consistency**\n\nThank you for your suggestion. We will revise the module names in the next version to ensure naming consistency.\n\n\n\n\n\n\nWe hope our responses were able to address any remaining concerns. Please do let us know if you have any further questions as well as what would be expected for score improvement. Even a slight increase in score would greatly help in recognizing the potential of our work. Thank you again for providing insightful comments."}, {"review_id": "9wL1EnUuxf", "reviewer": "Reviewer_RHTJ", "summary": "The 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.", "questions": "1. **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).", "limitations": "yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "The authors’ thorough rebuttal is appreciated. As I was already positive about the paper at the time of submission, I will retain my positive rating.", "rebuttal": "Thank you for your helpful feedback! We are grateful that the reviewer highlights our motivations, technical solution, and theoretical insights. To clarify, we summarize the original weaknesses or questions and adjust the order of the issues.\n\n### **Q1: Validation in low-resource scenarios (few-shot)**\n\nThank you for your suggestion regarding additional experiments under the setting of few-shot source data to further validate EDEN's robustness. \n\n- Modifications to the UDA setting\n\n  We have modified the original UDA setting to a few-shot UDA setting. Specifically, we retain only K samples per class from the original labeled source data, while keeping the unlabeled target data unchanged. \n  On the UCIHAR dataset, we conducted experiments with **5-shot, 10-shot, and 15-shot** settings.\n\n- Performance\n\n  As shown in the table, EDEN not only achieves excellent performance under the original UDA setting, but also exhibits strong robustness and generalization capabilities in the few-shot UDA scenarios.\n\n|             | 5-shot | 10-shot | 15-shot | ALL DATA |\n| ----------- | ------ | ------- | ------- | -------- |\n| Source-only | 20.67  | 38.06   | 40.19   | 75.12    |\n| RAINCOAT    | 24.65  | 39.69   | 47.36   | 94.43    |\n| ACON        | 18.36  | 54.55   | 63.77   | 97.02    |\n| EDEN        | 27.79  | 58.77   | 69.52   | 98.10    |\n\n### **Q2: Comparisons with TS-TCC and TimeMAE**\n\nTS-TCC and TimeMAE are time series self-supervised representation learning methods. In the UDA setting, we first perform self-supervised learning on the feature extractor using labeled source domain data and unlabeled target domain data. Subsequently, we fine-tune the feature extractor and classifier using labeled source data to perform classification on the target domain. We conduct experiments on the UCIHAR dataset.\n\n| Method      | UCIHAR |\n| ----------- | ------ |\n| Source-only | 75.12  |\n| TS-TCC      | 79.74  |\n| TimeMAE     | 81.66  |\n| EDEN        | 98.10  |\n\nCompared to the Source-only method, these representation learning methods extract generalized features through self-supervised learning. However, since the fine-tuning of the classifier still relies on labeled source data, the model tends to overfit the source domain, unable to mitigate domain shift, resulting in relatively limited performance gain. \n\nIn summary, these representation learning methods primarily focus on general representation learning capabilities, while EDEN, with strong general representation learning abilities, places more emphasis on transferable representation learning. We will include these discussions in the related work section and add these comparisons in the experiment section in the next version.\n\n### **Q3: Single-dataset time series classification**\n\nFollowing the setups and data splits of TS-TCC and TimeMAE, we conduct single-dataset classification experiments on the UCIHAR dataset.\n\n- Modifications to EDEN\n\n  Since only a single domain exists without domain shifts, we make minor modifications to EDEN. Specifically, we remove the domain discriminator in MSCA while retaining the curriculum mixing of coarse and fine-scale features. Additionally, we eliminate the transferability criterion in QFusion, performing feature fusion solely based on the discriminability criterion.\n\n- Performance\n\n  EEDEN achieves comparable experimental results in single-dataset time series classification compared to the advanced representation learning method TimeMAE and the general time series analysis method TimesNet [1], verifying its generalized feature extraction capability.\n\n  | Method   | Accuracy   |\n  | -------- | ---------- |\n  | TS-TCC   | 89.22±0.19 |\n  | TimeMAE  | 95.11±0.18 |\n  | EDEN     | 94.25±0.28 |\n  | TimesNet | 96.93±0.24 |\n\n  [1] Timesnet: Temporal 2d-variation modeling for general time series analysis. In *ICLR*, 2023.\n\n### **Q4: Visualizations of domain shift**\n\nWe adopt **t-SNE** to visualize the feature distributions on the UCIHAR dataset. However, **due to the limitations of the rebuttal format**, we're unable to upload images or external links. We can only provide **the Euclidean distances** $d$​ between the source and target features reduced to 2D space via t-SNE to show the domain gap. \n\nThe second column of the table represents the gap between the centers of the two domains, **reflecting the discrepancy between the feature marginal distributions**. The third column indicates the average gap between the centers of each class in the two domains, **reflecting the discrepancy between the feature conditional distributions**. \n\n|                   | $d$ between two domain centers | average $d$ between class centers |\n| ----------------- | ------------------------------ | --------------------------------- |\n| Source-only       | 3.89                           | 3.53                              |\n| with MSCA         | 1.74                           | 2.87                              |\n| with MSCA+QFusion | 1.31                           | 1.55                              |\n| with EDEN         | 1.02                           | 1.26                              |\n\nAfter applying MSCA to align the distributions, we observe a significant reduction in the discrepancy of marginal distributions, demonstrating that **MSCA effectively aligns the marginal distributions of the two domains**. However, a considerable gap in conditional distributions still remains. \n\nWhen we add QFusion, we observe a substantial reduction in the discrepancy in conditional distributions. This validates the motivation behind QFusion and confirms the fusion mechanism's effectiveness. Specifically, the feature quality of the same class varies significantly across different subspaces. By using transferability and discriminability criteria, we can adaptively select higher-quality features, thus **achieving better alignment at the class level**.\n\n\n\n### **Q5: Duplicate references in Lines 378 and 380**\n\nThank you for pointing out this error. We will correct it in the next version.\n\n\n\nWe hope our responses were able to address any remaining concerns. Please do let us know if you have any further questions as well as what would be expected for score improvement. Even a slight increase in score would greatly help in recognizing the potential of our work. Thank you again for providing insightful comments."}, {"review_id": "TEKk7P5TNb", "reviewer": "Reviewer_aFd7", "summary": "The 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.", "questions": "1. 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?", "limitations": "1. 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.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "The authors’ rebuttal and additional experiments have addressed most of the concerns. While the reviewer still feels that the novelty remains limited, and some evaluations could be stronger, the paper offers a clear and systematic approach. For these reasons, the reviewer has decided to raise the score to borderline accept.", "rebuttal": "Thanks for the helpful feedback! \n\n### **Q1: Modules are composed of well-known ideas with limited novelty**\n\nWe appreciate reviewer's insightful feedback regarding novelty assessment. Many high-level ideas (curriculum learning and fusion mechanism) are broad research paradigms, as evidenced by surveys [1-3]. However, **novelties of methods mentioned in surveys [1-3] are recognized in top venues (NeurIPS, ICML, ICLR)**. Hence, **solely using high-level ideas cannot limit novelty; the novelty lies in how to design special curricula and fusion mechanisms for special scenarios**. \n\n**Similarly, novelty of EDEN lies in special designs that address unique challenges of TSDA.**\n\n#### **1. Theoretical Insights**\n\nIn UDA theory (Eq. 2 in paper), existing TSDA methods fail to consider transferability ($d_{\\mathcal{H}\\Delta\\mathcal{H}}$) and discriminability ($\\lambda^*$) to design special curricula, fusion mechanisms and consistency regularization. EDEN bridge this gap.\n\n#### **2. Method-Specific Innovations**\n\n**MSCA: Theory-Inspired Curriculum Design**\n\n- Contrary to heuristic curricula, we propose the ***first coarse-to-fine scale curriculum*** to consider transferability and discriminability guided by TSDA theory.\n\n**Quality-Aware Feature Fusion: Theory-Inspired Fusion Mechanism**\n\n- Replaces heuristic fusion with a ***learnable feature scoring module*** based on transferability-discriminability trade-offs.\n\n**Temporal Coherence Learning: Consistency at Score-Level**\n\n- The first to leverage *TSDA theory-inspired scores* for consistency learning.\n\n[1] A survey on curriculum learning. TPAMI, 2021.\n\n[2] Curriculum learning: A survey. IJCV, 2022.\n\n[3] Deep multimodal data fusion. ACM computing surveys, 2024.\n\n### **Q2: Difference between explicit domains and multi-scale or multi-view learning**\n\n\"Explicit domains\" emerged naturally from this context: temporal dynamics are inherently implicit, while time series data are constrained by human-controlled explicit conditions.\n\n**Difference from multi-scale or multi-view learning**: EDEN's core contribution lies not in obtaining explicit domains but rather in how to use generated multiple explicit domains to tackle the unique challenge in TSDA: the entanglement of domain shift and intricate temporal patterns. This is beyond existing multi-scale or multi-view learning methods. \n\n- From terminological perspective: different connotations\n\n  - multi-scale vs. explicit domains\n    Multi-scale is one of three types of explicit domains, emphasizing different sampling rates, but it cannot cover multi-subspace and multi-segment included in our explicit domains.\n  - multi-view vs. explicit domains\n    Multi-view learning refers to datasets that contain different modalities or types of data, such as an object described by text, video, and audio [1], where multiple views **already exist** within the dataset. In contrast, explicit domains emphasize being artificially **derived from original time series** through changes in sampling rate, record duration, and time-frequency transform.\n\n- From the perspective of representation learning: different purposes\n\n  - multi-scale vs. explicit domains\n    The existing multi-scale learning approaches in time series leverage inherent **multiperiodicity** of time series, averaging forecasting series across multiple scales to enhance overall performance [2]. In contrast, multi-scale in EDEN emphasizes the **transferability differences of different-scale time series**, revealing that coarse-scale features exhibit better cross-domain transferability. This is a unique challenge in UDA that general multi-scale learning cannot address.\n\n  - multi-view vs. explicit domains\n    Existing multi-view learning in time series is to achieve self-supervised learning by utilizing **the consistency between different augmented data** [3]. However, EDEN is designed based on **UDA theory insight**. \n\n    $\\epsilon_{Q}(h)  \\leq \\epsilon_{P}(h)+ \\frac{1}{2}d_{\\mathcal{H}\\Delta\\mathcal{H}(P, Q)} + \\lambda^*$\n\n    In UDA theory, $d_{\\mathcal{H}\\Delta\\mathcal{H}(P, Q)}$ means transferability, while $\\lambda^*$ means discriminability. Both jointly determine UDA performance. From perspectives of **transferability and discriminability**, EDEN explores the relationships between different scales, subspaces, and segments.\n\n[1] Deep multi-view learning methods: A review. *Neurocomputing*, 2021.\n\n[2] Timemixer: Decomposable multiscale mixing for time series forecasting. *ICLR*, 2024.\n\n[3] Multi-view Self-Supervised Contrastive Learning for Multivariate Time Series. *ACMMM*. 2024.\n\n### **Q3: Performance gains are modest (4.8%).**\n\nFor comparison, we calculate improvements of methods relative to their respective SOTA at that time. Compared to baselines, EDEN achieves comparable performance gains. However, as field develops, performance will gradually approach its limits, and the rate of absolute improvement will also slow down. To further illustrate, we demonstrate performance gains of five general UDA methods relative to SOTA at the time of their publication. It is evident that the proportion of gains becomes smaller. However, this does not detract from the fact that these works are representative. \n\n| CLUDA    | CODATS   | RAINCOAT | ACON       | EDEN     |\n| -------- | -------- | -------- | ---------- | -------- |\n| 1.0%     | 0.7%     | 5.0%     | 8.9%       | 4.8%     |\n| DANN [1] | CDAN [2] | MDD [3]  | GVB-GD [4] | DALN [5] |\n| 2.24%    | 1.39%    | 1.37%    | 0.45%      | 0.44%    |\n\n[1] Domain adaptation with conditional transferable components. *ICML*, 2016.\n\n[2] Conditional adversarial domain adaptation. *NeurIPS*, 2018.\n\n[3] Bridging Theory and Algorithm for Domain Adaptation. *ICML*, 2019.\n\n[4] Gradually Vanishing Bridge for Adversarial Domain Adaptation. *CVPR*, 2020.\n\n[5] Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation. *CVPR*, 2022.\n\n### **Q4: Lack strong ablations: clarify each module's contribution and why all three modules' combination is optimal**\n\nWe respectfully note that this analysis is already provided in our submission: **In Table 2**, comprehensive ablation studies have been included,  containing **Module Ablation**, **MSCA Ablation** and **QFusion Ablation**. \n\nIn Module Ablation, we **not only investigate the contribution of each module, but also analyze their interaction**. \n\n- Each module's contribution\n\n  As detailed in **Line 292~295** of our paper and **Row 1~4** of Table 2, EDEN’s three modules show performance gains when added individually: MSCA yields an average absolute accuracy gain of 1.57 across three datasets; QFusion improves accuracy by 2.56; and TCL enhances performance by 1.30.\n\n- Optimal combination\n\n  As detailed in **Line 292~295** of our paper and **Row 1~8** of Table 2, using one or two of these modules achieves suboptimal results, and full integration of the three modules (EDEN) achieves best performance. \n  Against MSCA+TCL, EDEN improves +1.37 accuracy gain; against MSCA+QFusion, EDEN improves 1.24; against QFusion+TCL, EDEN improves 1.15. Thus, integrating all three modules emerges as the best choice.\n\n### **Q5: Limited computational cost analysis**\n\nIn **Appendix D.1** of our paper, we present training time of EDEN on FD dataset. \n\nWe additionally report GPU memory consumption, FLOPs (Floating Point Operations), and Parameters, where FLOPs quantifies overall computational complexity. Our method achieves comparable computational cost to SOTA baselines, while delivering significant performance enhancement. \n\n|                   | CDAN   | Raincoat | ACON   | EDEN   |\n| ----------------- | ------ | -------- | ------ | ------ |\n| Accuracy          | 90.56  | 86.75    | 91.74  | 98.05  |\n| Training Time (h) | 1.08   | 2.55     | 1.25   | 1.38   |\n| Memory (MiB)      | 715.6  | 859.6    | 725.6  | 789.3  |\n| FLOPs (MFLOPs)    | 467.7  | 123.2    | 233.8  | 235.3  |\n| params            | 228.6K | 768.6K   | 731.7K | 765.3K |\n\n### **Q6: Evaluation is limited to classification**\n\nForecasting constitutes a regression task, as is the mainstream approach (reconstruction) for anomaly detection. UDA problem for regression tasks remains unresolved.\n\nFirst, regarding the development of UDA, the target error bound of UDA theory holds exclusively for classification. Subsequently, many works have continued to improve classification UDA based on this bound [1, 2]. Occasionally, other fields, such as CV, have explored regression problems, but they have lost theoretical guarantees [3]. Currently, **there are few UDA methods that simultaneously address both DA classification and regression problems**. Researching on classification is the mainstream approach in DA field.\n\nSecond, TSDA methods (e.g. baselines in our paper) universally assume classification tasks unless explicitly stated otherwise, and there are no methods that can simultaneously address both classification and regression issues.\n\nOur paper focuses on theoretically-grounded design. MSCA and QFusion are designed inspired by the target error bound of UDA theory, which applies to classification tasks and is not suitable for forecasting or anomaly detection.\n\n[1] A theory of learning from different domains. Machine learning, 2010.\n\n[2] Bridging theory and algorithm for domain adaptation. ICML, 2019.\n\n[3] Representation Subspace Distance for Domain Adaptation Regression. ICML, 2021.\n\n### **Q7: Lack clear comparisons with more recent TSDA baselines**\n\nIn experimental section, we select five representative TSDA baselines published in the last five years: ACON(24), RAINCOAT(23), CLUDA(22), AdvSKM(21), and CODATS(20). Among these, ACON is SOTA. In Related Work, we provide detailed introductions and comparisons of these works. In addition to these works, if the reviewer could provide specific baselines that would facilitate further comparison, we would greatly appreciate it.\n\nPlease do let us know if you have any further questions."}]

Author rebuttal:

Meta-review: (a)  Summarize the scientific claims and findings of the paper based on your own reading and characterizations from the reviewers.
This paper addresses the challenge of Unsupervised Domain Adaptation (UDA) for time series data. The core thesis is that the fundamental hurdle in Time Series Domain Adaptation (TSDA) is the entanglement of domain shift with complex, partially observed temporal dynamics. The authors argue that a single, sensor-limited view of the data is insufficient to disentangle these factors. To overcome this, they propose EDEN, a framework that expands the original time series into three types of "multiple explicit domains" (multi-scale, multi-subspace, and multi-segment). EDEN integrates these domains via three coordinated modules:

Multi-Scale Curriculum Adaptation (MSCA): Aligns source and target domains progressively from coarse to fine scales.

Quality-Aware Feature Fusion (QFusion): Adaptively fuses temporal and frequency features based on learned transferability and discriminability scores.

Temporal Coherence Learning (TCL): Enforces consistency across adjacent segments.
The paper claims that by enriching representations through these multiple explicit views, EDEN can better approximate the underlying implicit temporal dynamics, leading to more effective domain-invariant learning. Comprehensive experiments on six benchmarks reportedly show state-of-the-art performance, with an average accuracy improvement of 4.8% over existing methods.

(b) Strengths of the Paper

Compelling and Well-Motivated Problem: The paper identifies a clear and significant challenge in TSDA—partial observability and entangled domain-temporal dynamics—and proposes a novel conceptual framework ("multiple explicit domains") to address it.

Systematic and Cohesive Framework: The proposed EDEN framework is not a collection of disjoint tricks but a thoughtfully designed system where three modules, each targeting a different type of explicit domain, work in concert. The alignment of these modules with theoretical insights (transferability vs. discriminability trade-off) is a strength.

Strong Empirical Validation: The paper demonstrates consistent and significant performance improvements across a wide range of established time series benchmarks. The results are convincing and clearly superior to a solid set of baselines.

Thorough Rebuttal and Additional Analysis: The authors were exceptionally responsive during the rebuttal phase. They addressed reviewer concerns comprehensively by adding new experiments (few-shot UDA, single-dataset classification, new baselines like TS-TCC and TimeMAE), providing theoretical and empirical justifications for design choices, and offering quantitative visualizations.

(c) Weaknesses and Missing Elements

Perceived Novelty (Addressed): A primary initial weakness, raised by one reviewer, was the perception that the constituent ideas (curriculum learning, feature fusion, consistency) are established. However, the authors successfully argued that the novelty lies in the specific, theory-guided instantiation of these ideas for the unique challenges of TSDA (e.g., the first coarse-to-fine curriculum based on transferability/discriminability theory for TSDA). This concern was largely mitigated.

Scope of Evaluation: While the evaluation is extensive for classification tasks, the paper does not explore the framework's applicability to other time series tasks like forecasting or anomaly detection. This remains a potential area for future work but does not detract from the contributions made in the classification setting.

Terminological Hurdle: The introduction of the term "explicit domains," while useful internally, initially created a minor barrier to understanding. The authors effectively clarified its meaning and distinction from related concepts like multi-view learning in their rebuttal.

(d) Reasons for Decision (Accept as Poster)
The most important reasons for acceptance are:

Technical Soundness and Empirical Success: The paper presents a technically sound, well-motivated method that delivers a clear and significant empirical advance over the state-of-the-art in its domain. The performance gain of 4.8% is substantial in this field.

Successful Rebuttal: The authors' rebuttal was a model of how to engage with reviewers. They convincingly addressed all major concerns, transforming initial reservations into support. The additional experiments and analyses significantly strengthened the paper.

Strong Reviewer Consensus: After the rebuttal, all three reviewers converged on a positive assessment. Two reviewers (KFEc and RHTJ) maintained their positive scores, and the third (aFd7), who had the most significant concerns, explicitly raised their score to borderline accept based on the authors' responses. A clear majority positive consensus is a strong indicator of acceptance.

This work is an excellent contribution worthy of a poster presentation. It systematizes a novel approach to a difficult problem and delivers top-tier results. While it introduces valuable ideas, its incremental yet solid nature places it more appropriately as a strong poster rather than a spotlight/oral presentation, which are typically reserved for paradigm-shifting or exceptionally groundbreaking work.

(e) Summary of Discussion and Rebuttal

Reviewer KFEc (Initial Score: 4): Concerns included clarity of the abstract/introduction, justification for the curriculum learning assumption, and handling of different time scales. The authors promised textual revisions, provided additional quantitative evidence (A-distance metrics) to justify the curriculum, and clarified standard practices for handling differing resolutions. The reviewer was satisfied and maintained their positive score.

Reviewer RHTJ (Initial Score: 4): Requested validation in low-resource (few-shot) settings, comparison with representation learning baselines (TS-TCC, TimeMAE), and visualization of representations. The authors conducted extensive new experiments: few-shot UDA (showing strong performance), added the requested baselines (showing EDEN's superiority in the UDA setting), and provided quantitative metrics from t-SNE analysis (Euclidean distances between domains) to demonstrate the effect of each module. The reviewer was satisfied and maintained their positive score.

Reviewer aFd7 (Initial Score: 4, raised from 3): This was the most critical review, citing limited novelty, terminological issues, and modest gains. The authors mounted a robust defense: they argued that true novelty lies in the specific, theory-guided design for TSDA, not just the use of high-level ideas. They differentiated "explicit domains" from multi-view/scale learning both terminologically and in purpose. They contextualized the 4.8% gain as strong within the advancing field of UDA and provided additional computational cost analysis. The reviewer acknowledged that the rebuttal addressed "most of the concerns" and raised their score, though they retained a minor reservation on novelty.

The authors' responses to Reviewers KFEc and RHTJ were definitive and closed those issues entirely. For Reviewer aFd7, while a subjective disagreement on the degree of novelty may persist, the authors provided compelling arguments and evidence that the community consensus (as seen in other top publications) aligns with their view of contribution. The significant score increase from this reviewer indicates the response was effective. Therefore, the overwhelming evidence and post-rebuttal consensus support acceptance.
Decision: Accept (poster)