AdaTS: Learning Adaptive Time Series Representations via Dynamic Soft Contrasts
Authors: Denizhan Kara
Tomoyoshi Kimura
Jinyang Li
Bowen He
Yizhuo Chen
Yigong Hu
Hongjue Zhao
Shengzhong Liu
Tarek F. Abdelzaher
Venue: NeurIPS 2025 poster ()
Keywords: Time Series
Representation Learning
Contrastive Learning
Self-Supervised Learning
Adaptive Learning
Dynamic Temporal Modeling
Primary area: general_machine_learning
TL;DR: AdaTS introduces a dynamic soft contrastive learning framework for adaptive time series representations, leveraging frequency-domain similarities with dynamic temporal and ordinal instance contrasts.

Abstract: Learning robust representations from unlabeled time series is crucial, and contrastive learning offers a promising avenue. However, existing contrastive learning approaches for time series often struggle with defining meaningful similarities, tending to overlook inherent physical correlations and diverse, sequence-varying non-stationarity. This limits their representational quality and real-world adaptability. To address these limitations, we introduce AdaTS, a novel adaptive soft contrastive learning strategy. AdaTS offers a compute-efficient solution centered on dynamic instance-wise and temporal assignments to enhance time series representations, specifically by: (i) leveraging Time-Frequency Coherence for robust physics-guided similarity measurement; (ii) preserving relative instance similarities through ordinal consistency learning; and (iii) dynamically adapting to sequence-specific non-stationarity with dynamic temporal assignments. AdaTS is designed as a pluggable module to standard contrastive frameworks, achieving up to 13.7% accuracy improvements across diverse time series datasets and three state-of-the-art contrastive frameworks while enhancing robustness against label scarcity. The code will be publicly available upon acceptance.

AdaTS: Adaptive Time Series Representation Learning through Dynamic Contrasts
Denizhan Kara", Tomoyoshi Kimura", Jinyang Li Bowen He Yizhuo Chen", Yigong Hu", Hongjue Zhao", Shengzhong Liu", Tarek F. Abdelzaher' "University of Illinois Urbana-Champaign TShanghai Jiao Tong University (kara4, tkimura4, jinyang7, yizhuoc, yigongh2, hongjue2, zaher)oillinois.eer (hebowen1, @s edu.c.
Abstract
Learning robust representations from unlabeled time series is crucial, and con- trastive learning offers a promising avenue. However, existing contrastive learn- ing approaches for time series often struggle define meaningful similarities, tending to overlook inherent physical correlations and diverse, sequence-varyine non-stationarity. This limits their representational quality and real-world adapt- ability. To address these limitations, we introduce AdaTS, a novel adaptive soft contrastive learning strategy. AdaTS offers computationally efficient solution centered on dynamic instance-wise and temporal assignments that enhance time series representations by: (i) leveraging Time-Frequese Coherence provide robust, physics- -uided similarity measurements; (ii) preserving relative instance similarities through ordinal consistency learning; and (iii) adapting to sequence- specific non-stationarity with dynamic temporal assignments. AdaTT is designed as pluggable module for standard contrastive frameworks, achieving accuracy improvements of up to 13.7% across diverse time series datasets and three state-of- the-art contrastive frameworks while enhancing robustness under label scarcity.
Introduction
Self-supervised learning (SSL) has emerged as a transformative paradigm for extracting meaningful representations from large-scale unlabeled data, achieving remarkable success in domains like computer vision 7 25) and natural language processing [38]. Recently, SSL has garnered significant attention in time series (TS) analysis. particularly for Internet of Things (IoT) applications (37 68 29). which present unique challenges data variability, non-stationarity. and labeling difficulty. As popular SSL approach, contrastive learning (CL) structures semantically meaningful representations by distinguishing positive (similar) and negative (non-similar) sample pairs.
In TS data, representations from consecutive sampling periods are naturally correlated; nearby timestamps tend to have similar values because the underlying physical processes evolve smoothly
(e.g., movement patterns in activity recognition). With this property, existing works [68 [6] have proposed CL objectives by defining temporally close samples as similar and minimizing their geometric distances in the latent embedding space.
However, determining accurate similarity measures for TS CL faces multiple challenges: First, existing similarity metrics for TS often fail to effectively capture inherent physical correlations. These correlations arise from common underlying physical mechanisms, allowing signals from similar events activities to be meaningfully compared even when separated in time, occurring under varied conditions, or originating from different instances (e.g., different individuals performing the same action or distinct machines of the same type in similar operational states). Many standard TS similarity
*Coresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(TFO) Tempora
Prior work: Fixed temporal soft assignment decay for all sequences
Instance wise
Does notuelize
simlarlyy
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SdaTS: Adaptive temporal soft assignment N,T based sequence dynamics
sequence
similantty
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AdsT3 assigns closer tempural soft assignment thaniyg.
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Instance-wise Ordinal Consistency
Dynamic Assignnment
Figure I: Overview of AdaTS framework. Best viewed in color.
methods (e.g., cosine, Euclidean) overlook underlying physical signal properties and susceptible to noise 16 31. As a widely used TS distance metric, Dynamic Time Warping (DTW) 49 15
enables temporal alignment between sequences but is computationally expensive and impractical for iterative training on large-scale datasets. Moreover, DTW is sensitive to noise and pathological alignments in high-resolution data, limiting its general applicability in extensive IoT applications
[52 47). Prior works like FastDTW [50] have attempted to optimize DTW's 's computational efficiency but the cost of accuracy degradation on high- nigh-freuuecc data.
Second, defining consistent similarity relations must account for non-stationary TS characteristics. Real-world signals often exhibit substantial variability not only within single sequence but also across different sequences, where statistical properties and temporal dynamics evolve due to factors like motion patterns, environmental changes, or varying system states [35 Existing CL frame- works often miss this inter-sequence variability, either by assigning all negative pairs or temporal relationships uniformly [63] 17 68), or by adopting the same decay functions for temporal similarity (e.g., a single sigmoid function for temporal similarity decay for all sequences) (4. Such static
approaches fail to adapt the varying rates of change and temporal unpredictability inherent in different sequences even within the same dataset.
To address these challenges, we propose AdaTS, adaptive soft contrastive learning strategy. AdaTS is a flexible, pluggable module designed to augment existing CL frameworks by dynamically modeling both instance- wise physical similarities and sequence-spert
temporal characteristics with minimal computational overhead. Figure [ provides an overview of AdaTS: a time-series similarity metric (green arrows) establishes temporal and cross-sequence relations. The similarities across different sequence samples guide an ordinal consistency loss for instance-wise learning of soft similarity relations across sequences, while similaritier within samples of each sequence introduce dynamic temporal assignment mechanism (right panel) that adapts temporal similarity weights based on each sequence's 's unique temporal variability, Its core contributions are:
While AdaTS's components can integrate various TS similarity metrics, we leverage Time- Frequeeyyy Coherence (TFC) (70) primary similarity measure. TFC robustly quantifies similarity in the frequency domain by analyzing harmonic structures. We apply it within our framework, which provides key advantages such computational efficiency and robustness for TS similarity relations (see Section 2.2p.
1. Physics-Guided TimeeFFrqueecyy Similarity:
2. Instance-wise Ordinal Consistency Learning: Leveraging TS sample similarities across se- quenees, we propose an ordinal consistency loss (Section 2.3). Instead enforcing absolute similarity values in time series contrasting, which can be noisy and context-dependentt this loss preserves the relative ordering of physical similarities between samples. This self-supervised approach differs from methods using progressive augmentation intensities to learn ordinal relationships [30] or requiring explicit ordinal labels 2 by deriving relations directly from signal properties.
3. Dynamic Temporal Similarity Assignment: Leveraging temporal similarities within TS se- quences, we introduce a dynamic temporal assignment mechanism (Section 2.4) that adapts to the varying dynamics of different sequences. This mechanism quantifies the average adjacent dissim- ilarity of each sequence to dynamically adjust temporal similarity weights within contrastive learning. This approach reflects the varying temporal dynamics and non-stationarities inherent to each sequence, allowing a more robust and accurate TS similarity relation.

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Algorithm TIMEEREQQUENY CONEERENCE
Require: Datasct = (X Xx), X, Txxxx
N sequences with M T N samples
1: Phase 1- Compute STFT and auto- spectral densities
for 2i do
3: 24 f.. < (xF)(x).)
b Time- Time-frequency representation b Auto-spectral density
4: Su, < [x,(t,
5: end for
Phase 2 Compute pairwise coherence across all samples
7: + MMM
b Initialize coherence matrix
8: for =ri=10 M do
9: for for to M do
(t, f) t (f (t, (S 11: Ci + LF En, (,,01)),,(177
10:
Cross-spectral density
1
Average TFC.
12: end for
13:
end for
14:
return
M M pairwise coherence similarity
We extensively evaluate AdaTS across six TS datasets, demonstrating its ability augment existing methods and enhance performance. Specifically, AdaTS improves average CL accuracy by 7.3% (up 13.7%), enhances robustness dynamic variations, and achieves superior performance at low label rates. Our results show that AdaTS effectively incorporates underlying physical and temporal correlations and adapts to the varying sequence dynamies common real-world TS applications.
2 AdaTS Framework
This section presents the AdaTS framework. We first define the problem setting and then detail its three complementary components: (i) Time-Frequency Coherence (TFC), (ii) Ordinal Consistency Learning, and (iii) Dynamic Temporal Assignment.
2.1 Problem Definition
We address the problem of learning nonlinear embedding function fo given a dataset X (XI...., N,} of N time series sequences. Each sequence X; E TXxXCC comprises T sam- ples, (x......,, ..rr). where each sample X,, R4CC is a fixed-length window of L sensor readings from C' channels. The objective is learn fo : RLXC + Rd mapping each sample x,, to an H -idimensional representation ri, fo(
2.2 Time-Frequency Coherence
A critical challenge in time series similarity analysis is handling non-stationarity, where time series' statistical properties (e.g., mean or variance) evolve over time due to structural shifts or external influences. Such variations manifest as dynamic trends, changing noise levels, shifts in frequency content 0 To efficiently handle such dynamic time series properties, we leverage Fime-Frequency Coherence (TFC) the similarity measure that enforces spectral consistency. TFC ensures robust, efficient signal similarity across both time and frequency domains. By capturing harmonic alignments with FFT-based operations, TFC overcomes the computational overhead of DTW and remains resilient to noise. TFC captures spectral relationships that often correlate with underlying physical processes (e.g., harmonic structures in vehicle engines or periodic patterns in human movements), providing meaningful way to compare signals across varying conditions without restricting AdaT'S from using other similarity metries.
TFC addresses the limitations of conventional time series similarity metrics like DTW three key ways: (i) It provides computational efficiency through FFT-based operations, making it suitable for large datasets with high sample rates 19 24 (ii) It captures spectral relationships without introducing
non-linear temporal deformations that can distort signal characteristics [50] [52] (iii) It demonstrates robustness to -on-stationarity and noise (24) and generalizes to different domains
58 40 46).
We detail TFC in Algorithm [ consisting of two main phases. In Phase 1, we compute the Short- Time Fourier Transform (STFT) for each sample x; to obtain their time-frequency representation (t,t, f). We then calculate the auto-spectral density Sttt, f) for each sample, which represents its

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Algorithm 2 ORDINAL CONSISTENCY LOSS
Require: Datasct embeddings R fri, rach ri E R: Similarity matrix D RMMMM margin
1: T - [(b,j,b]] D) < D(i, k), vi,j, E [1]
Valid triplets with semantic ordering
2: d( t- Irs ryll2
>. Euclidean distance
3: Loc f- [T (L.AAET max(0, d(i d(i, k) + 6)
b Enforce d(t.,j) + 5 I d(i, k)
4: return Loc
Average loss over all valid triplets
power spectral density. In Phase 2, we compute the cross-spectral density Siz (t, between each pair of samples i and which captures the similarity between two samples from time- free-frey representations. We also calculate the time-frequency coherence C25 ft, between samples i and by normalizing the cross-spectral density with the auto spectral densities. We use magnitude-squant coherence following the standard TFC definitions [1] 770 24], which intentionally discards sign information to quantify linear correlation strength. Ct f) represents the similarity between the frequency components of samples at time and frequency f. Finally, we average the coherence values across all time-frequee freeufeuy bins to calculate the time-frequency coherence Cij-
TFC provides a semantically meaningful measure by capturing harmonic structures without introduc- ing non-linear distortions. It is computationally efficient, sample-rate agnostic, and well-suited for large-scale datasets with varying frequency resolutions. Additionally, TFC offers a straightforward measure to capture the change in relevant dynamies within time series sequences. We use these properties to enhance the ordinal consistency learning and dynamic temporal assignment of AdaTS.
2.3 Ordinal Consistency Learning
Traditional CL approaches like InfoNCE
contrast all instances within a batch via hard negatives. However, directly applying hard negative sampling TS data can be problematic, as it overlooks the inherent temporal sample similarities. To address this, prior works (34) use time series similarity metries (e.g., DTW) structure the representation space by enforcing semantic similarity to reflect the data-level similarity. However, this assumption does not always hold. First, data-level similarity metrics are inherently context-dependent, whose effectiveness varies based on the characteristics of the dataset and the specific domain. These metrics could introduce biases that can limit generalization across different types of time series data. Second, due the non-stational stationttonnry nature of time series signals and the prevalence of noise, these metrics may fail to accurately capture the underlying corre- lations between events and true physical phenomena. Consequently, enforcing semantic similarity to match TS similarity metrics can corrupt the representations and struggle to generalize across diverse TS datasets. For example, vehicle signals recorded in real-world environments often contain substantial noise, which can distort metric-based similarities. Under such scenarios, using distorted values to enforce strict semantic alignment could severely corrupt the representations. To mitigate this, we propose ordinal consistency loss to restrict the relative ordering of similarities, which remains mostly consistent within the same data context and, therefore, more generalizable.
42
Algorithm details the ordinal consistency loss. Given sample embeddings R = {ri, rM} and their similanty matrix D E MMMxM. valid sample triplets T that preserve relative similarity ordering are generated. Then, Euclidean distances dii,j) j) are computed between embeddings r; and ry. The ordinal consistency loss Loc is then calculated as the average margin ranking loss over valid triplets T, ensuring the positive-to-referene distance is less than the pegative-torrferrene distance. Triplet generation is implemented efficiently with vectorized tensor operations for minimal overhead.
The ordinal loss ensures that embedding distance orders align with the physical characteristics of samples. Unlike methods relying on absolute similarity values (which can be unreliable due noise, context-dependencyy and varying physical factors like motion or environmental conditions), our ordinal consistency loss focuses solely on the relative ordering between sample pairs. This approach is well-suited for physical signals, as relative distance orders provide more stable, reliable indicators of semantic similarity across diverse conditions than absolute similarity measurements.
2.4 Dynamic Temporal Assignment
Compared to prior works that employ static temporal similarity assignments 34 that time series sequences within the same dataset can exhibit varying non-stationarity and different rates of change. While tuning a "sharpness" parameter 7r for a decay function can help, this one- fize-fits-all parameter still forces the model to globally apply the same setting on faster-changing and
63
we observe

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Algorithm 3 DYNAMIC TEMPORAL CONTRASTIVE LOSS
Require: Datasct X
(X,,..,, XN) where X, T similarity function D; embedding function fo-
1: Let ri, = 21,++2 and Fs,t 7i,4 be embeddings from two augmentations of X; timestamp
2: Phase 1 Compute pairwise similarity for each sequence
3: for v = to N do
4: D E " < ((...
5: end for
6: Phase 2 Compute similarity decay rate for each sequence
7: for n to N do
8: R²" +---
--1 --
2 1 log [t,
D Xn similarity decay rate
end for
10: K" + BatchNorrm(r")
11: % Phase 3- Temporal soft assignment with dynamic decay
12: forn= ===1 to N do
13:
()( 2 (x"TT) (t-
Soft assignment for timestamps and e'
(t,,, + exp(es,
14:
b Softmax
- exp (mp(Fceorca)
3
15:
(i, +- - (pr, ++))
D. Compute CL positive pairs Compute negative pairs
16:
(i,t) -- Σ w/ (1) log (t,
+f(1,47)
17:
((1)) = = (i, L (1,
Loss for each timestamp
18:
end for
2N return Lr ANT
(i.t)
19:
slower-changing sequences alike, often resulting suboptimal modeling of rapid signal variations. Instead, we propose dynamically assigning varying degree of temporal similarity weighting to each sequence based on its intrinsic temporal characteristics, enabling the framework to accommodate different levels of dynamics more effectively. This adaptive strategy can better model the temporal evolution of signals with varying rates of change.
Inspired by Schreiber's method [51] to exploit information in time series similarities, we propose calculating statistic R² for each sequence X,, detect physically relevant temporal differences and changes via dissimilarity of adjacent samples to adapt the temporal similarity assignment. The intuition behind xm is to measure the overall temporal unpredictability within sequence. A high x² indicates that, on average, adjacent time steps have low similarity (high dissimilarity), suggesting the sequence exhibits rapidly changing dynamics or generally less predictable. Conversely, low x² indicates high average similarity between adjacent steps, suggesting smoother transitions more predictable temporal structures. This statistic allows for dynamic adaptation of temporal similarity assignments for TS sequences by identifying sequences with rapid changes or stable regions.
Algorithm summarizes the dynamic temporal assignment process. In Phase 1, given the dataset X' [X, } and a similarity function D (e.g.. TFC), we first compute the intra-sequence pairwise similarity DJd for each sequence X,, D is a T T matrix where each element d represents the similarity between samples at timestamps t and t' in sequence X,, In Phase 2, we calculate the average adjacent dissimilarity statistic x~ for each sequence X., to measure its inherent temporal unpredictabiligg For each sequence, we compute the negative logarithm of the similarity between adjacent timestamps. This transformation maps similarity values (typically in [0, 1)) to positive dissimilarity measure: low similarity (near 0) yields a large positive value, while high similarity (near 1) results small positive value. Averaging these transformed values yields x². representing the sequence's ' average dissimilarity per transition. Using the negative logarithm makes k" more sensitive to instances of very low adjacent similarity compared to a direct average. few steps with near-zero similarity will dramatically increase n² which is desirable to detect sharp drops in coherence and significant changes dynamies. Normalization across the batch then applied to ensure the statistic k" is comparable across different sequences.
In Phase we calculate the temporal soft assignment weights w., f') for each pair of timestamps (t, t'') through a sigmoid function. Our approach extends SoftCLT [3 by introducing sequence- specific dynamic decay rate n² Tr. Here, the base rate Tr modulated by each sequence's 's average adjacent dissimilarity statistic n² This dynamic decay rate allows the model to adapt the temporal similarity assignment based on the sequence's inherent unpredictability. Sequences with higher ne
5

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Table I: Finetune results on different datasets. We mark the best results.
ACIDS
MOD
PAMAP2
RWHAR
Average
Metric
Acc
F1
Acc
F1
Ace
F1
Acc
F1
Acc
F1
SW-T [39
0.9187
0.7829
0.8935
0.8919
0.8462
0.8148
0,9031
0.9018
0.8904
0.8479
TST
0.6785
0.5010
0.5820
0.5371
0.7469
0.6692
0.7928
0.6757
0.7001
0.5958
MAE
25
0.8521
0.6908
0.7817
0.7793
0.7382
0.6999
0.86338
0.8700
0.8089
0.7600
AudioMAE
27
0.7845
0.6120
0.7274
0.7249
0.7808
0.7478
0.8163
0.7437
0.7773
0.7071
CAVMAE
22
0.7995
0.6711
0,5432
0.5266
0.7995
0.6711
0.9113
0,9153
0.7634
0.6960
CMC
54
0.7836
0.6452
0.9049
0.9023
0.7571
0.7223
0.8211
0.8384
0.8167
0.7771
TNC 551
0.8352
0.7372
0,8533
0.8539
0.8013
0.7506
0.8817
0.8784
0.8429
0.8050
Cosmoo
+
0.8776
0.7298
0.3228
0,3241
0.7910
0.7469
0.8529
0.7968
0.7111
0.6494
SimCLR
0.5658
0.4879
0.7535
0.7434
0.7346
0.6635
0,7830
0.7181
0.7092
0.6532
TF-C 71
0.7863
0.6448
0.5787
0.5712
0.6593
0.6058
0.7998
0.7049
0.7060
0.6317
MF-CLR 14
0.8343
0.6587
0,8058
0,8042
0.7445
0.7045
0.7940
0.7954
0.7947
0.7407
Informer 02
0.9470
0.8455
0.8972
0.8961
0.8746
0.8660
0.9313
0,9353
0.9125
0.8857
LIMU-BERT 65
0.5556
0.3712
0.4297
0.3970
0.7781
0.7554
0.8120
0.7508
0.6439
0.5686
TS2Vec [68]
0.7703
0.6312
0.7380
0.7350
0. 0.6069
0,5280
0.6117
0.6019
0.6817
0.6240
TS2Vec SoftCL
0.7753
0.6679
0.7495
0.7509
0. 0.704
0,6272
0.8226
0.7909
0.7630
0.7092
TS2Vec AdaIT
0.8379
0.7211
0.7810
0.7825
0.7772
0.7266
0.8794
0.8877
0.8189
0.7795
TS-TCC [66]
0.8758
0.7400
0.7709
0.7744
0.7871
0.7107
0.8684
0.8227
0.8256
0.7620
TS-TCC SoftCLT
0.8925
0.7784
0.7894
0.7865
0.8034
0.7323
0.8875
0,8680
0.8432
0.7913
TS-TCC AdaTS
0,9155
0.8027
0.8135
0.8120
0.8257
0,7764
0.8984
0.8712
0.8633
0.8156
FOCAL K7
0.9347
0.8272
0.9548
0.9540
0.8438
0.8243
0.9261
0.9327
0.9148
0.8846
FOCAL + SofCLT
0.9425
0.7991
0,9564
0.9554
0. 0.8498
0.8298
0,9383
0,9433
0,9218
0.8819
+ Ada'TS
0.9571
0.8480
0.9705
0.9700
0.8648
0.8560
0.9527
0.9568
0.9363
0.9077
(more unpredictable, lower adjacent similarity) will experience a faster decay of temporal similarity weights, while smoother sequences (lower x") will have a slower decay. Since contrastive loss can we define a softmax probability Pr (t, t')) of the relative similarity against all other pairs when computing loss. Following similar convention, we use simplified indexing e.g., Ti,+T,, Ti,++22 to refer to the concatenated augmented views of the same sample. Lastly, we compute the weighted contrastive loss for each sequence where the positive pairs are timestamp pairs separated by time lag of T, and negative pairs consider other timestamps in the sequence weighted by the soft assignment weights (t. t').
be interpreted as the cross-entropy loss 33 34
The final AdaTS contrastive loss combines ordinal consistency Loc and temporal contrastive Lr components, to be combined with the original loss from the base SSL framework:
AddaTs = AvcCoc + MLT,
(1)
where Age, A E [0, 1] control the contribution of each loss.
3
Evaluation
We present our experimental setup and extensive evaluations to demonstrate AdaTS's performance and efficiency. We further ablate AdaTS to understand the contributions of its components.
3.1 Experimental Setup
Datasets. We extensively evaluate AdaTS across two IoT application domains using four datasets: Hu- man Activity Recognition (PAMAP2 48), RWHAR 53 and Moving Object Detection (MOD (37. ACIDS 62 We segment time-series data into samples of fixed-size windows, with lengths de- termined by each dataset's temporal characteristics. For multi-modal data, AdaTS processes each modality independently to accommodate varying sampling rates and resolutions, aligning with our channel-wise soft assignment mechanism. We partition each dataset into training, validation, and test sets using an 8:1:1 ratio. Detailed dataset descriptions and configurations are provided in Appendix A
Baselines and Backbone Model. We compare AdaTS with 12 SOTA baselines spanning con- trastive [54] 43 71 masked reconstruction 25 65 27 22]. Transformer-basee based 69 [39], and tem-
SSL frameworks. Details are provided in Appendix B We employ SWIN- Transformer (SW-T)
poral 68
16
55
37
39 as the backbone, which computes local attention within sliding windows across input spectrogram patches. For fine-tuning, we append a linear classification layer to the pretrained representations. Training details and configurations are available in Appendix C and

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TLoss 152vec
T52Vect SonCuI
FOCAL +ADATS
TSTCC
90
INC
15-1CC
152Vec+ Ada
+ADATS
+ADATS
Accuracy
80
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Distance Classification Speed Classification
125 UCR Datasets
29 UEA Datasets
(a) Distance and Speed Classification
(b) UCR/UEA Datasets
Figure 2: Classification performance across MOD tasks (left) and UCR/UEA datasets (right).
T52Vec
TS2Vec AdaTS
TS-TCC
T5-TCC AdaTS
FOCAL
FOCAL AdaTS
1.0
1.0
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Accuracy
0.8
0.8
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0.8
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0.6
0.6
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100%
10%
100%
1%
100%
10%
100%
1%
ACIDS
MOD
PAMAP2
RWHAR
Figure 3: Accuracy with or without AdaTS across different label ratios (100%, 10%, 1 1%).
3.2 Evaluation Results
Fine-tuning Results. Table TolleToompgg compares AdaTS's performance with other self-supervised learning frameworks across different datasets. Results show that AdaTS improves performance across all three contrastive frameworks. When integrated with Vec, AdaTS achieves the largest average improvement of 13.7%, while integration with FOCAL yields the best absolute performance with a 2.1% accuracy gain.
The HAR datasets (PAMAP2 and RWHAR) benefit most from AdaTS due their relatively lower complexity and bandwidth compared to vehicle classification tasks [1] 62], enabling better capture and differentiation similarity relations. In contrast, the Vehicle Classification datasets (MOD and ACIDS) show less improvement due to their high dynamics and complex non-stationarities from environmental conditions, which challenge instance-wise similarity capture. However, AdaTS's dynamic temporal assignment mechanism and frequency-awarr similarity enable more robust CL representations compared to existing baselines and SoftCLT, leading to better performance. Moreover, observe that FOCAL and TS2Vec benefit relatively more from AdaTS compared to TSTCC. This because the TS-TCC framework already built upon leveraging the temporal correlation of sensing signals through temporal contrasting views. Hence, the additional temporal contrastive loss in AdaTS does not provide as much additional information as does for FOCAL and TS2Vec, which are more focused on CL objectives through orthogonality relations and hierarchical feature learning.
We evaluate AdaTS -aggmettdd CL baselines on additional downstream tasks of distance and speed classification on the MOD dataset. Figure ((a) shows AdaTS consistently improves embedding quality across all baselines, lcading to better downstream performance. Notably, we observe larger gains on speed classification tasks, which require more fine-graines temporal pattern recognition based sequence dynamics. This demonstrates that AdaTS's ' dynamic temporal assignment mechanism and ordinal consistency effectively capture varying motion patterns, enabling better speed differentiation in non-stationary sequences. Qualitative assessment through t-SNE visualizations (Appendix (.I)) further confirms that AdaTS produces more distinct and well-separated clusters across all datasets, particularly improving separation for complex, high-frequency dynamics in ACIDS and MOD.
We further evaluate AdaTSS on TS classification tasks using 125 UCR archive univariate datasets [12 and 29 UEA archive multivariate datasets (3]. We implement AdaTS with TS2Vec using the SoftCLT evaluation code (34). Figure 2(b) shows that AdaTS consistently outperforms the baselines across both UCR and UEA datasets. The performance gains over SofiCLT are smaller compared to IoT datasets, since UCR and UEA datasets exhibit more stationary patterns with less noise and dynamics. Consequently, the dynamic temporal assignments provide less additional information compared to loT datasets. Nevertheless, AdaTS's superior performance demonstrates its robustness and adaptability across different datasets.
Label Efficiency in Fine-tuning. Next, we evaluate AdaTS with limited fine-tuning labels. In Figure we present the accuracy curves of different CL. baselines with and without AdaTS using 100%, 10%, and 1% of training labels. We observe that CL frameworks with AdaTS consistently
7

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1.0
Optinal
Agcurag
0.9
Optmal
0.00
3.99
Optmal
0.8
0.6
0.7
0.6
1.0
0.8
(a) ACIDS Ane
(b) PAMAP2 Aoe
(c) ACIDS AT
(d) PAMAP2 Xr
Figure 4: Ordinal consistency (Dac) and dynamic assignment (Ae) hyperparameter analysis (TS2
2,92
Optanul
Opattel
Optime
3.06
1.02
5.75
0.2
0.
2.00
0.2
1.2
2.94
35
355
3.04
3.0
25
0
(a) ACIDS
(b) PAMAP2-
(c) ACIDS TT
(d) PAMAP2 -T
Figure 5: Sensitivity analysis for ordinal consistency margin (6) and dynamic temporal assignment temperature (Tr) (FOCAL).
outperform their counterparts across all label ratios, validating AdaTS's capability in improving the performance of SSL frameworks even with limited labels. Notably, there are consistent performance improvements across all label ratios, with the largest gains usually observed at 1% labels. This is due to the increased difficulty in learning meaningful representations with fewer labels, where AdaTTSs instance-wiss temporal relationships and dynamic assignment mechanisms provide more granular information to guide the embedding space.
Robustness Evaluations. We evaluate AdaTS's robustness under challenging conditions including noise corruption and extreme class imbalance. Comprehensive experiments with Gaussian corruption at multiple levels (a 0.1 to 1.0) demonstrate that AdaTS consistently outperforms baselines, with Fl improvements up to +7.31% % on ACIDS under extreme noise (detailed in Appendix D.3). TFC's spectral coherence analysis filters noise while preserving signal correlations, and ordinal consistency maintains relative similarity relationships when absolute metrics fail. Beyond noise robustness, also evaluate performance under extreme class imbalance. While ordinal consistency is robust to moderate data variations, extreme class imbalance
cause embedding space shrinkage when similar instances are over-represented. We address this by applying distance regularization following the VICReg anti-collapse framework 44. Controlled oversampling experiments (Appendix D..4 show that distance regularization recovers 52% of performance loss under extreme 10x oversampling conditions, effectively maintaining embedding space diversity. We recommend applying distance regularization when dataset imbalance exceeds 5x oversampling ratios.
Sensitivity Analysis. We analyze the sensitivity of AdaTS to key hyperparameters: ordinal con- sistency weight Ape. dynamic temporal assignment weight Aς, ordinal consistency margin (Algo- rithm 2. and dynamic temporal assignment temperature Tr (used in conjunction with Algorithm 3
Figure presents the analysis for Age and A on ACIDS and PAMAP2 datasets. Increasing Age prioritizes instance-wise relationships across sequences. We observe that both ACIDS and PAMAP2 benefit from higher Ane values, with PAMAP2 showing more consistent improvements due to its lower sample complexity and dynamics. This allows for more effective capture of granular similarities through ordinal consistency. On the other hand, increasing X enhances the dynamic temporal assignment mechanism, which particularly benefits ACIDS due to its complex dynamics and higher non-stationarity. The dynamic assignment helps capture varying temporal relationships in sequences with diverse motion patterns and environmental conditions.
Figure presents sensitivity analysis for the ordinal consistency margin 8 and dynamic temporal assignment temperature Tr. The ordinal consistency margin ò controls the separation enforced between positive and negative pairs in the ordinal triplet loss. For ACIDS, with highly dynamic, high-frequency signals, smaller ó values (e.g.. 0. 1-0.2) preserve fine-grained similarity distinctions crucial for differentiating similar events. For PAMAP2, with smoother, lower-frequency signals, slightly larger (e.g., 0.2) helps enforce clearer separation in the embedding space. Overall, moderate values (8 E [0.1, 0.2)) yield robust performance. The dynamic temporal assignment temperature TT modulates how strictly the temporal similarity decay influences the temporal contrastive loss. For ACIDS, characterized by continuous vehicle motion, moderate to higher Ty values (c.g., 0.5-1.0)

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Table 3: Distance Function Overhead.
Table 2: Compute Pretrain Overhead Comparison.
Model
TS2Vec
AdeTT
TS-TCC
AddTTS
FOCAL
AddTS
Dist Func
Euclidean
Cosine
DTW
TFC
(MB)
2685
2787
2797
2863
2670
Time (s) 0.206
0.245
10.895
0.245
Time 69)
0.231
0.243
0.257
0.271
0.252
0.245
Mem. (MB) 7.8
7.8
19.6
7.8
Table 4: AdaTS component ablation results.
Table 5: AdaTS similarity metric results. Best
results in bold, second-best underlined.
PAMAP2
ACIDS
Backbone
Components
Acc
F1
Acc
F1
Backdone
Sim. Function
PAMAP2
ACIDS
Baseline
0.6069
0.5280
0.7703
0.6312
Acc
Acc
noOrd
0.6782
0.6101
0.7964
0.6992
TFC
0.7772
0.7266
0.3379
0,7211
TS2Vec
noTemp
T-StaticTemp
0.7393
0.7527
0.6700
0.7006
0.7890
0.8153
0,7073
0.6857
TS2Vec
Buclidean
DTW
0.7469
0.7575
0.7106
0.6853
0.7837
0.7998
0.6644
0.6740
AdaTS
0.7772
0.7266
0.8379
0.7211
Cosine
0.7244
0.6861
0.8066
0.6904
Buseline
0.8438
0.8243
0.9347
0,8272
TFC
0.8648
0,9571
noOrd
0.8454
0,8309
0.9415
FOCAL
DTW
0.8482
0.8408
0.9443
0.8381
FOCAL
noTemp
0.8537
0,8380
0.9500
0,8361
Fuclidean
0,8592
0.8468
0,9324
0,8289
TStaticTemp
0.8551
0,8417
0.9464
0.8326
Cosine
0,8403
0.8339
0.9475
0.8419
AdaTS
0.8648
0.8560
0.9571
0,8480
perform well. For PAMAP2, with more abrupt motion changes, lower moderate Ty values (e.g.. g.... 0.5) are effective. The robust performance across Tr E [0.5, 1.0] indicates that while -ataset-dependent, precise tuning is not overly critical. These results demonstrate that AdaTS's complementary compo- nents effectively adapt to diverse temporal characteristics, enabling robust representation learning across different contexts.
Computational Overhead. We evaluate the computational overhead of AdaTS by comparing it with the vanilla CL counterparts. Table faaee2ssoon shows the model sizes, pretraining overhead, and inference time for each model. The results demonstrate that AdaTS introduces minimal computational overhead (0.26-0.36% in size, 5.19-6.60% in pretraining time) while significantly improving performance. Moreover, since soft assignments are only performed during pretraining, AdaTS does not introduce additional inference time overhead, making suitable for applications requiring low-latency inference.
Table compares the time and memory overhead of different distance functions, including Euclidean, Cosine, DTW, and TFC ACIDS dataset. Results show that AdaTS's TFC similarity metric maintains comparable computational overhead to simple metrics like Euclidean and Cosine similarity while providing better performance through frequency-dome -oomai analysis. Additionally, compared to GPU-optimized DTW, TFC achieves over 40x speedup in pretraining processing time and 2x reduction in memory overhead, particularly beneficial for high-bandwidth data. Furthermore, systematic scalability experiments across varying sequence lengths (2s-168) demonstrate that AdaT'S maintains consistent 3-7% overhead with linear scaling characteristics (detailed analysis in Appendix D.2)
3.3 Ablation Studies
This section evaluates AdaTS's key components through ablation studies on ACIDS and PAMAP2
datasets, using both TS2Vec and FOCAL as backbones.
Component Analysis. Table 4 analyzes AdaTS's core components by comparing five configurations: (i) Baseline (base model: TS2 FOCAL); (ii) noOrd (adds Dynamic Temporal Assignment (DTA), excludes Ordinal Consistency (OC)); (iii) no noTemp (adds OC, excludes Temporal Assignment (TA)); (iv) w.StaticTempp (OC+ Statie TA (STA), akin SoftCLT);; and (v) Ada'TS (full: OC + DTTA). Components are added to the baseline. Results show that both OC (seen in noTemp vs. Baseline) and TA (seen in noOrd vs. Baseline) individually contribute to performance improvements actoss both backbones. Comparing wStatic Temp (OC + STA, akin to SoftCLT's s temporal mechanism) with the full AdaTS (OC + DTA) highlights the additional benefit of our dynamic temporal assignment, particularly on the more non-stationary ACIDS dataset. Ordinal consistency (no'Temp) tends to yield larger gains on PAMAP2, with its lower frequency characteristics, by effectively capturing relative physical similarities in different activities. In contrast, dynamic temporal assignment (comparing AdaTS to wSStaticTemp) shows larger gains on ACIDS, which exhibits higher dynamics diversity and frequency complexity. This demonstrates the complementary nature of AdaTS's components, enabling to effectively handle diverse temporal characteristics and enhance models incorporating both simple (c.g., TS2Vec) and complex (c.g., FOCAL) temporal constraints.

---

Effect of Similarity Functions. Taalle the effectiveness of different similarity metrics D in Algorithms I and for capturing temporal relationships. On PAMAP2, Euclidean distance performs better than DTW and Cosine similarity, demonstrating that simple point-wise metrics can be effective for pover-frequenc signals with more regular temporal patterns. On the other hand, Cosine similarity shows better performance on ACIDS due to its robustness to amplitude variations in signals with higher dynamics. TFC outperforms these conventional metrics on both datasets through its generalizable and robust frequency-domadl analysis. To validate TFC's design choice of STFT over alternative time- freeefryy transforms, we conducted systematic comparisons with wavelet transforms (Appendix E). Results demonstrate TFC's superior generalizability across diverse datasets (+2. accuracy on PAMAP2, +1.94% on MOD), while wavelets show specialized advantages only in highly transient scenarios.
4 Related Work
Sele-Supe Learning (SSL). SSL generally categorizes into contrastive learning (CL) 23
8,5 [2) [36), masked autoencoders (MAE) (25) 21 [59 (8), and others. CL, which maximizes similarity of representations of positive pairs while minimizing that of negative pairs, has been widely adopted for its transferability and generalizability. Apart from treating augmented views as positive pairs (similar) in the unimodal context, CL. has also been extensively applied to multimodal data, considering samples from different modalities as positive pairs
73
Contrastive Learning in Time Series. For TS analysis, various designs of augmentations [64] and contrasting strategies [16] 17 66
9 61 have been proposed to achieve better performance with CL, based on temporal correspondences of TS data. For physical sensing, CL has shown significant advancement using multimodal sensor data from wearable devices [43 26 13) Most CL methods for TS analysis assign the same weight to all negative pairs and compute a hard contrastive loss.
67
68
71 55
Soft Contrastive Learning. CL typically discriminates each instance from others and treats all negative pairs equally. That often overlooks the properties of instances and pushes similar instances farther in the embedding space. Consequently, multiple approaches have been proposed to employ soft inter-sample relations [88 20 60). In the TS domain, CL also faces significant challenges due to the complex nature of temporal correlations and non-stationary signal characteristics. Recent works like StatioCL [63] and SoftCLT (34) have attempted to address these limitations through non-stationarity-awaa aware and soft similarity measures, but their reliance on sequence-leve processing and caching mechanisms limits scalability and granularity. In contrast, our approach introduces efficient and adaptive similarity measures that dynamically respond to both physical and temporal characteristics of the data, enabling more robust representation learning at scale.
Discussion and Conclusions
In this paper, we introduced AdaTS, an adaptive strategy for time-series contrastive learning that effec- tively captures physical correlations and non non-stationary characteristics in time series. By leveraging Time-Frequency Coherence physics-guided similarity measure to enhance ordinal consistency loss and dynamic temporal assignments, AdaTS achieves up to a 13.7% accuracy improvement across multiple datasets while maintaining computational efficiency. Its pluggable design enables seamless integration with existing contrastive learning methods, enhancing robustness, particularly in limited-label scenarios. Our extensive evaluation further highlights AdaTS's adaptability varying sampling rates and diverse IoT applications with dynamic signal patterns. Future work includes addressing the "oversampling" problem in ordinal consistency loss, where frequent sampling of similar instances may shrink embedding space distances. In the Appendix F we provide additional pointers and discuss the limitations of AdaTS.
Acknowledgements
This work was partially supported by NSF CNS 20-38817, the JUMP2 SRC program, and the Boeing Company.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Yes, we claim AdaTS as pluggable module that enhances the performance contrastive learning frameworks for time series learning through novel and efficient physics-infol infsoms-informed similarity metrics. Our extensive evaluation in Section 3 explores and supports both AdaTS's downstream performance improvement and minimal overhead.
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Justification: The paper discusses the limitations and future work in Section 5 and Ap- pendix F
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8. Experiments compute resources
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For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
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Appendix
The appendix of this paper is structured as follows.
Section
A
introduces the evaluated datasets with detailed specifications and preprocessing
details.
Section
details the compared baselines in our experiments.
Section introduces the used backbone model structure and their related configurations.
Section presents comprehensive evaluation results including oversampling robust-
ness experiments and computational scalability analysis.
Section provides systematic comparison between wavelet transforms and STFT-based TFC, demonstrating complementary strengths.
discusses limitations including STFT assumptions and extreme non-stationarity, along with future work directions.
Section F
S
describes the training configurations and experimental details.
Section
discusses the broader impacts.
A
Datasets
In this section, we provide detailed descriptions and specifications of each dataset. T comprehensive statistics including modalities, sampling frequencies, and segmentation details.
A.1 Dataset Specifications
Table 6: Detailed Dataset Specifications and Preprocessing
Dataset
Modalities (Freg)
Sample Leugth
Overlap
#Labels
Downstream Task
ACIDS
acoustic,
(102511<)
27,595
Classiication
MOD
37
acoustic (HKH2), seismic (100H2)
2 sec
0%
7.335
Multi-task: Vehicle (7), Distance (4). Speed (4)
PAMAP2
gyro, mag, light (50Hz)
5
50%
Activity Classification
RWHAR
34
acc, gyro, mag (100Hz)
2 sec
50%
12,887
Activity Classification (8)
Segmentation Approach. We segment raw time-series data into fixed-length samples using sliding
windows. Window sizes and overlap ratios are chosen based on each dataset's 's temporal characteristics to capture meaningful patterns while maintaining sufficient training samples. Each segment serves as an independent sample for both pretraining and downstream classification tasks.
UCR/UEA Benchmark Archives. We additionally evaluate on standard time series classification benchmarks:
UCR Archive (univariate): 125 datasets, 166,500+ total samples, with average sequence lengths ranging from 50 to 2,844, covering medical, motion, and sensor domains.
UFA Archive (multivariate): 29 datasets, 47,000 total samples, with average sequence lengths ranging from 8 to 5,730, covering HAR, medical, and industrial domains.
These archives exhibit more stationary patterns compared to our primary IoT datasets, yet AdaTS demonstrates consistent improvements (Figure 2b in the main paper), validating robustness across diverse temporal dynamics.
comprises IMU recordings from 9 participants performing 18 diverse physical activities, including outdoor sports, household tasks, and locomotion exercises. Sensors were positioned on three body locations (chest, dominant wrist, and dominant ankle), each recording three-axis accelerometer, gyroscope, and magnetometer readings at 100 Hz. Our experiments utilize data exclusively from the wrist-mounted sensor. We employ leave-one-out evaluation strategy, allocating seven randomly selected participants for training and two for testing, enabling cross-subject generalization assessment.
PAMAP2 48
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RealWorid-HAR (RWHAR) 53 captures multimodal sensor data from 15 participants executing 8 everyday physical activities spanning locomotion, postural transitions, and stationary positions. The dataset encompasses accelerometer, gyroscope, magnetometer, and light sensor measurements recorded at 100 Hz from waist-mounted devices. Our evaluation focuses exclusively on this waist sensor data to maintain consistency with practical deployment scenarios. Following leave-one-out protocol, we partition the dataset using ten randomly selected subjects for training, two for validation, and three for testing.
ACIDS 41 10 (Acoustic-Seisee) IDentification System) represents a comprehensive vehicle classification benchmark comprising over 270 data collection runs from 9 distinct ground vehicle types operating across environmental conditions developed by the U.S. Army Research Lab. Data acquisition employed two co-located acoustic and seismic sensor systems that captured vehicles traversing constant speeds from approach through closest point of approach (CPA) to departure. Acoustic signals were band-pass filtered (25-400 Hz) and digitized at 1025 Hz using 16-bit A/D) conversion. Collection parameters varied substantially: CPA distances ranged from 25 m to 100 m. while vehicle speeds spanned 5-40 km/h depending on vehicle type and terrain. This variability introduces significant domain shift challenges between training and testing partitions. We employ an 8:1:1 random split at the run level for training, validation, and testing respectively.
(Moving Object Detection) is a multimodal vibration dataset designed for moving target detection and classification. Data collection utilized sensor nodes integrating HaspberryShake 4D geophones and microphone arrays deployed two distinct sites: a repurposed research facility and a parking environment. The geophone technology demonstrates superior sensitivity to nearby vibrations compared to conventional smartphone accelerometers. Seven target classes were captured spanning pedestrian and vehicular categories with diverse propulsion systems (electric, combustion engine, off-road variants). Each target traversed the vicinity of sensor nodes at varying speeds while sensors captured synchronized seismic (100 Hz) and acoustic (16 kHz, downsampled to 8 kHz) signals. Collection sessions lasted 40 to 60 minutes per target type. The dataset exhibits challenging real-world variability in target speed, distance, and environmental conditions. We partition samples randomly at the sample level with an 8:1:1 ratio for training, validation, and testing.
MOD 37
B Baselines
Below, we outline the baselines used in our evaluation.
CMC 54 CMC is contrastive learning framework that leverages multiview data by treating different modalities as distinct views. It minimizes the geometric distance between representations of the same sample across modalities while maximizing the distance between representations of different samples.
Cosmo 43 Cosmo generates multimodal time-series representations using contrastive fusion mechanism that maps modality embeddings onto a hypersphere. Similar features are treated as positive pairs, while dissimilar features are considered negative, facilitating cross-moda alignment.
SimCLR 7 SimCLR contrasts samples across different augmented views. For each sample in the batch, random augmentations are applied to generate two unique views, which are treated as positive pairs. SimCLR brings representations of the same sample closer the feature space while pushing apart the representations of other samples in the same batch to learn discriminative features.
MAE 25
Masked Autoencoder (MAE) uses an encoder-decodes architecture to learn represen- tations from masked reconstruction. During pretraining, a significant portion (e.g.. 75%) of the input from each modality is masked. Then, separate encoders for each modality are used extract multimodal features. After encoding, the features are fused via MLP layers, and the fused embeddings are decoded back to spectrogram inputs for reconstruction.
TNC [55]: TNC contrasts samples within the temporal neighborhood against temporally distant samples. It treats temporally close samples (neighbors) positive pairs and other samples in the batch as negative pairs, with a discriminator that predicts the sample's probability of being a neighbor.
LIMU-BERT 65 LIMU-BERT extends BERT's self-supervised learning approach to unlabeled IMU data, tailoring it to sensor-specific temporal patterns. Custom modifications enable it to effectively capture sequential information unique to IMU signals.
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leveraging Transformer with both global and local attention mechanisms for audio representation. Audio is segmented into spectrogram patches, with portion masked to enable efficient encoding. The model incorporates learnable embeddings for masked patches and a decoder with localized attention for spectrogram reconstruction.
AudioMAE 277
AudioMAE builds upon the MAE framework 25
CAV-MAE [22]: CAV-MAE combines the principles of MAE and contrastive learning process audio-visual data. It employs separate encoders for each modality alongside a joint encoder for cross-modal feature fusion. Multi-stream processing enhances input reconstruction and cross-modal learning
TS2VEC 68 TS2VEC enhances time-series representations through contrastive objectives across hierarchical window sizes. Positive pairs are formed by augmenting the same sample and its context, while negative pairs are derived from different samples sequences.
TS-TCC [16]: TS-TCC captures time-series representations by combining cross-view predictions with temporal and contextual contrastive learning. The framework generates two augmented views of each sample and extracts context vectors using an autoregressive model. These context vectors predict future timestamps in the alternate view to learn temporal alignment and context awareness.
FOCAL 37|: FOCAL is a contrastive learning framework specifically designed for multimodal time-series data. FOCAL factorizes the representation into shared and private subspaces to in- corporate modality-comme
and modality-unique representations. The subspaces are enforced to be independent with geometric orthogonality constraints. To capture temporal correspondence, temporal ranking constraint enforces temporal locality.
Time-Series Transformer (TST) 669| A Transformer- basee framework for multivariate time series representation learning. It uses a standard Transformer encoder architecture and is trained with self-supervised masked reconstruction task.
Originally proposed for computer vision, this hierarchical Vision Trans- former builds representations by starting with small patches and gradually merging them. We adapt for time-series spectrograms, using its self-super pre-training capabilities.
Swin Transformer 33]
C Backbone Encoder
We implement the SWIN- Transformer (S--T),a Vision Transformer (ViT) variant 33 as the primary backbone encoder for AdaTS. We adapt it specifically for processing freuerre spectrograms derived from time-series data. Input spectrograms, potentially from multiple sensing modalities, are first segmented into non-overlgpping patches. Each patch is then linearly embedded form sequence of embedding vectors. SW-T processes these patch embeddings through multiple stages. Each stage consists of SW-T blocks that apply self-attention within local, non-overlapping windows. To enable cross-window connections, shifted window mechanism is employed in successive blocks. As the network depth increases, patches are progressively merged, reducing the spatial resolution while increasing the feature dimension, allowing the model to learn hierarchical representations. This design, compared to the original ViT, enhances computational efficiency while capturing both local and global contextual information from the spectrograms. For multi-modal datasets, separate SW-T encoder can process the spectrogram of each modality independently. The AdaTS framework. including its TFC similarity metric and ordinal consistency loss, typically operates on these modality- specific feature representations before any late-stage fusion.
D Additional Evaluation Results
In this section, we provide additional evaluation results that are not included in the main paper.
D.1 Representation Visualization
To qualitatively assess the quality of the learned representations, we employ t-SNE (56 to visualize the embeddings generated by the FOCAL backbone and FOCAL enhanced with AdaTS. Figure 6 presents these visualizations for the ACIDS, MOD, PAMAP2, and RWHAR datasets. In these plots, different colors correspond to distinct ground-truth labels.
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ACIDS
MOD
(a) FOCAL
(b) FOCAL + AdaTS
(C) FOCAL
(d) FOCAL + AdaTS
PAMAP2
RWHAR
(e) FOCAL
(f) FOCAL AdaTS
(g) FOCAL
(h) FOCAL AdaTS
Figure 6: t-SNE visualization of embeddings different datasets. For each dataset (ACIDS, MOD, PAMAP2, RWHAR), comparisons are shown for FOCAL (left subfigure of pair) and FOCAL + AdaTS (right subfigure pair). Different colors represent different ground-trute labels. AdaTS- enhanced models generally lead to better separated and more cohesive clusters. Best viewed in color.
The visualizations generally indicate that integrating AdaTS with FOCAL leads to more distinct and well-separated clusters, suggesting an improved ability to capture the underlying data structure. For instance, the ACIDS and MOD datasets, which are characterized by complex, high-frequency dynamics, the embeddings from FOCAL + AdaTS (Figures 66 and (d1) exhibit clearer separation between classes compared the baseline FOCAL embeddings (Figures 64 and This improvement highlights the efficacy of AdaTS's Time-Frequeee
Coherence (TFC) similarity measure and dynamic temporal assignment for disentangling complex temporal patterns.
On the RWHAR dataset (Figures and 6h). AdaTS further refines the representations, resulting in tighter clusters for the different human activities compared to FOCAL alone. This can be attributed to the ordinal consistency loss, which helps preserve fine-grained relative similarities between instances. The PAMAP2 dataset (Figures 6 and 6). with its larger number of activity classes and inherent inter-class similarities, presents a more challenging scenario. While some overlap between classes persists with AdaTS, there is noticeable improvement in the cohesion of individual clusters and a reduction in overlap for several classes, indicating that AdaTS's components contribute positively in such complex settings.
Overall, these t-SNE visualizations qualitatively confirm that AdaT'S enhances the representational power of the FOCAL backbone, yielding feature spaces that better reflect the intrinsic structure of time-series data across diverse application domains.
D.2 Computational Scalability Across Sequence Lengths
To comprchensively evaluate AdaTS's computational scalability, conducted systematic experi- ments measuring sequence processing times across varying lengths (2s, 4s, 8s, 16s) for three different SSL frameworks. 7 the detailed results.
The results demonstrate that AdaTS maintains consistent overhead between 3% and 7% across all sequence lengths and frameworks, providing strong evidence of excellent scalability. Importantly, overhead remains relatively stable even as sequence length increases by 8x (from to 16s), indicating that AdaTS's computational complexity scales linearly with the base framework without introducing quadratic or higher-order growth. This linear scaling behavior confirms that the O(T) complexity of our Kn statistic computation (described in Section 2.4 and the efficient FFT-based
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Table 7: Sequence processing time (ms) and AdaTS overhead across varying sequence lengths.
AdaTS maintains consistent 3-7% overhead demonstrating linear scalability.
Framework
Metric
2s
4s
16s
Base Time (ms)
2.60
5.13
10:40
20.37
FOCAL
+AdaTS Overhead
+0.16 (6.2%)
+0.31 (6.0%)
+0.46 (4.4%)
+0,96 (4.7%)
Base Time (ms)
2.47
5.07
9.89
20.83
VS2
+AdaTS Overhead
+0.11 (4.5%)
+0.21 (4.1%)
+0.43 (4.3%)
+0.76 (3.6%
Base Time (ms)
2.60
5.44
11.44
24.52
TS-TCC
+AdaTS Overhead
+0.09 (3.5%)
+0.24 (4.4%)
+0.49 (4.3%)
+0.82 (3.3%)
Table 8: Noise robustness under Gaussian corruption (a noise std.). AdaTS consistently outperforms
FOCAL baseline.
Dataset
Method
a 0.1
a = 0.2
a = 0.5
0 = 1.0
Acc
F1
Acc
F1
Acc
F1
Acc
F1
FOCAL
0.8425
0.8302
0.8494
0.8370
0.8562
0.8464
0.8557
0.8448
PAMAP2
+AdaTS
0.8596
0.8488
0.8584
0.8493
0.8612
0.8508
0.8648
0.8587
FOCAL
0.9338
0.7947
0.9429
0.8199
0.9365
0.7809
0.9146
0.7859
ACIDS
+AdaTS
0.9479
0.8629
0.9520
0.8375
0.9384
0.8456
0.9374
0.8590
TFC operations (Section 2.2 enable practical deployment across varying sequence lengths and temporal resolutions. The slight variation in overhead percentages across frameworks (3.3-6.2%) reflects differences in their baseline computational characteristics, but all remain within a narrow and acceptable range for real-world applications.
D.3 Noise Robustness Analysis
To evaluate AdaTS's robustness under noisy conditions, conducted systematic experiments with Gaussian noise corruption at multiple levels (a = 0.1, 0.2, 0.5, 1.0 relative to signal standard deviation) on the PAMAP2 and ACIDS datasets.
T comprehensive results comparing the FOCAL baseline with FOCAL + AdaTS across different noise levels. FOCAL + AdaTS consistently outperforms the baseline across all noise levels, with F1 improvements up to +7.31%% % on ACIDS under extreme corruption (a = 1.0). While FOCAL's reequency-domatee processing provides baseline robustness PAMAP2 (where Gaussian noise can act as regularization for low-dynamic activities), AdaTS's dynamic temporal supervision becomes
crucial for high-frequency ACIDS data.
TFC's s spectral coherence analysis effectively filters noise while preserving meaningful signal cor- relations. The frequency-domain approach inherently provides robustness by focusing spectral patterns rather than point-wise amplitude values that are more susceptible additive noise. Fur- thermore, ordinal consistency maintains relative similarity relationships when absolute metrics fail under extreme noise corruption. By preserving the ordering of instance similarities rather than exact distances, OCL ensures that the learned representations remain semantically meaningful even when individual similarity measurements become unreliable due to noise.
These results demonstrate that AdaTS's design choices-frequente frequency-domain domain analysis through TFC and ordinal consistency ruption, making it suitable for real-world deployments where sensor data quality varies significantly.
inherent robustness environmental noise and signal cor-
D.4 Robustness to Extreme Class Imbalance
To evaluate AdaTS's robustness under extreme class imbalance scenarios, conducted controlled oversampling experiments on the PAMAP2 dataset. We systematically oversampled the three most frequent activity classes (walking, ironing, lying) at ratios of I xx 2x, 5x, and 10x, resulting in overall dataset size increases from baseline to +315%.
Table presents the results of these experiments. We observe that extreme oversampling (5x+) causes performance degradation in both baseline FOCAL and FOCAL + AdaTS due embedding space shrinkage, where overreepresnntation of similar instances collapses the embedding space. Specifically,
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Table 9: Robustness extreme class imbalance on PAMAP2. Distance regularization (DistReg) effectively prevents embedding collapse under extreme oversampling.
Method
1x (Baseline)
2x (+35%)
5x (+140%)
10x (+315%)
Acc
Ace
F1
Acc
F1
Acc
F1
FOCAL
0.8427
0.8258
0.8378
0,8318
0.8200
0.8154
0.8063
0.8014
+AdaTS
0.8637
0.8535
0.8422
0.8331
0.8154
0.8039
0.8016
0.7985
+AdaTS + DistReg
0.8639
0.8510
0.8504
0.8424
0.8385
0.8362
0.8339
0.8241
at 10x oversampling, vanilla AdaTS accuracy drops from 0.8637 to 0.8016 (.).1)) while baseline
FOCAL drops from 0.8427 to 0.8063 (-3.64%).
To mitigate this issue, we apply cosine-based distance regularization following the VICReg anti- collapse framework 4 The results demonstrate that FOCAL AdaTS DistReg maintains robust performance even under extreme imbalance: at 10x oversampling. achieves 0.8339 accuracy, recovering 52% of the performance loss compared to vanilla AdaTS. This validates the effectiveness of distance regularization for preventing embedding space collapse under extreme imbalance conditions, as discussed in Section
E
Wavelet Transform vs. STFT-based TFC Analysis
To address potential limitations of STFT's local stationarity assumption, we conducted comprehensive experiments comparing Continuous Wavelet Transform (CWT) with Morlet wavelets an alternative to STFT in our TFC similarity computation. This appendix presents a detailed analysis of their complementary strengths across different signal characteristics.
E.1 Overall Performance Comparison
Table 10 presents the performance comparison between wavelet-based AdaTS and STFT-based TFC AdaTS across all primary datasets using identical experimental conditions and hyperparameters.
Table 10: Performance Comparison: Wavelet Transform vs. STFT-based TFC
ACIDS
PAMAP2
RWHAR
MOD
Method
Acc
F1
Acc
F1
Acc
Ace
Wavelet AdaTS
0.9525
0.8346
0.8438
0.8353
0.9533
0.9577
0.9511
0.9496
TFC AdaTS
0.9571
0.8480
0,8648
0.8560
0.9527
0.9568
0.9705
0,9700
Improvement
+0.46%
+1.34%
+2.10%
+2.07%
-0.06%
-0.09%
+1.94%
+2.04%
TFC outperforms wavelets on most datasets (ACIDS: +1.78% F1, MOD: +1.94% Ace, PAMAP2:
+2. 10% Acc), while wavelets show competitive performance RWHAR. This demonstrates TFC's superior generalizability across diverse signal characteristics.
E.2 ACIDS Case Study: Dynamic-Basee Analysis
To understand the complementary strengths of both approaches, we analyzed ACIDS vehicle classifi- cation by categorizing vehicles based movement dynamics. Table II presents results grouped by vehicle dynamics.
Table ACIDS Vehicle Category Classification by Dynamics
Category
Vehicle Types
Wavelet Ace
TFC Ace
Best
#Samples
Heavy vehicles
1, 8,
94.7%
96.6% %
TFC (+1.9%)
937
Medium vehicles
3,5
81.8%
82.7% %
TFC (+0.9%)
313
Light vehicles
6.7
91.3 %
87.5%
Wavelet (+3.8%
104
Key Findings:

---

TFC advantages: Consistent performance on heavy and medium vehicles (largest sample sizes: 937 and 313 samples) with steady, predictable motion patterns. TFC's frequency- domain coherence effectively captures consistent spectral relationships in these scenarios.
Wavelet vdvattaee: Superior performance light vehicles (104 samples) exhibiting highly agile maneuvers and rapid acceleration changes, where wavelet's multi-resolution analysis better captures transient dynamics.
E.3
Individual Vehicle Type Breakdown
Table 12 provides granular analysis at the individual vehicle type level, revealing systematic perfor- mance patterns.
Table 12: Individual Vehicle Type Performance Analysis
Type
Category
Wavelet
TFC
Best
Adv.
Samples
Rationale
Type
Heavy
92.9%
98.5
THC
+5.6%
326
Consistent patterns favor enherence
Type
Heavy
93.9%
96.6
TFC
+2.7%
179
Low-frequency signatures
Type
Heavy 97.0%
99.3%
TFC
+2.3%
134
Steady, low-dynamics movement
Type
Light 60.9%
43.5%
Wavelet
+17.4%
23
Highly agile, rapid maneuvers
Analysis: TFC demonstrates superior performance across the largest sample sizes (Types 2, 8, 9), indicating strong generalizability. The largest wavelet advantage (+17.4% for Type 7) occurs in the smallest sample size (23 samples) with the most dynamic vehicle type, suggesting a specialized rather than generalizable advantage for extreme transient scenarios.
E.4 Implications and Recommendations
Our findings reveal systematic complementary strengths:
1. TFC's broad applicability:
Superior performance across general cases, steady patterns, and larger datasets. Recommended as the default choice for most time-series representation learning scenarios.
2. Wavelet's specialized strength: Advantages in highly dynamic scenarios with abrupt changes (e.g., rapid maneuvers, mechanical faults). Beneficial for applications specifically targeting transient phenomena.
3. Unified framework potential: AdaTS can leverage both approaches by selecting the appropriate fime-frequency transform based on signal dynamics, potentially achieving best-of-both-worldd performance.
Computational Considerations: While wavelets offer theoretical advantages for non-stationary signals, STFT-based TFC provides superior computational efficiency through FFT optimizations, making it more practical for large-scale deployments. Future work could explore adaptive selection mechanisms that choose between STFT and wavelet transforms based on detected signal characteris- tics.
F Limitations and Future Work
F1
Ordinal Consistency Oversampling
One potential limitation of the proposed instance-wise ordinal contrastive loss is the "oversampling" problem: if a group of very similar or closely related instances is sampled much more frequently than others, their calculated similarity may remain extremely close, concentrating ordinal pairs and shrinking distances in the embedding space. Our experiments (Section 2.3 and the added evaluations Appendix (D) demonstrate that distance regularization following the VICReg framework [4] effectively mitigates this issue, recovering 52% of performance loss under extreme 10x oversampling conditions. We recommend applying distance regularization when dataset imbalance exceeds a 5x oversampling ratio.
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F.2 STFT-Based Limitations for Extreme Non-Stationarity
While our STFT-based TFC approach effectively captures time-frequency characteristies across diverse datasets, it inherently assumes local stationarity within analysis windows. This assumption may limit performance in scenarios with extreme instantaneous -on-titiion
Highly Transient Signals: Signals with abrupt, discontinuous changes (e.g., sudden mechanical faults, seismic events, rapid maneuvers) may not be optimally characterized by STFT"s fixed- resolution time-frequency decomposition. Our wavelet analysis (Appendix [D demonstrates that CWT with Morlet wavelets can provide advantages in highly agile scenarios (Type 7 vehicles: +17.4% improvement), though TFC maintains superior generalizability across most cases.
The current K,, statistic measures average adjacent dissimilarity, which may not fully capture instantaneous step changes or sudden environmental shifts. While effective for gradual non-stationarity (as demonstrated on ACIDS and MOD datasets), extreme instantancous changes represent an edge case requiring specialized treatment.
Extreme Instantaneous Dynamics:
F.3 Future Directions
Adaptive Time-Frequency Representations: Future work will explore advanced representations like wavelet transforms, Wigner-Vill Ville distributions [57], adaptive selection mechanisms that choose between STFT and wavelets based on detected signal characteristics. This could enable AdaTS to leverage the complementary strengths of different time-frequency decompositions.
Enhanced Temporal Statistics: Develop more adaptive and dynamic statistics derived from temporal similarities. Incorporating real-time anomaly detection or event-driven weighting could enhance adaptability to sudden signal changes, particularly beneficial for streaming data applications where non-stationarity patterns evolve unpredictably.
Streaming and Online Adaptation: Extend AdaTS for real-time streaming scenarios with incre- mental Kn computation (O(1) complexity per sample as discussed in the rebuttal). This would enable edge IoT deployment for responsive applications like activity recognition transitions or predictive machinery maintenance.
G Training Configurations and Experiment Details
This section details the training strategies, hyperparameter settings, and implementation specifics for pretraining and the-tuning models with AdaTS. The primary configurations are summarized in Table 13 and SW-T model-specific parameters are Table I4
Table 13: General Training Configurations for AdaTS.
Parameter
Pretraining
Fine-tuning
Optimizer
AdamW
Adam
Weight Decay
0.05 1c-4 (Default)
0.05
Max Learning Rate
1e-2 (Default) N/A
Min Learning Rate (Pretrain)
le-7
Learning Rate Scheduler
Cosine Annealing N/A N/A 10
Step Decay
LR Decay Factor (Tine-tuning)
0.2
LR Decay Period (Fine-tuning)
50 epochs N/A
Warmup Epochs (Pretrain)
MOD, ACIDS: 2500 RWHAR, PAMAP2: 1000 256 (64 sequences) 4 0.1 (Default for CI.)
Epochs
200
Batch Size
128
Sequence Length (Tezg) 'emmpeaturre (T)
N/A
N/A
During pretraining, we employ the AdamW optimizer with cosine annealing learning rate scheduler, including brief warmup period. We randomly sample batches consisting of 64 sequences, where each sequence comprises 4 consecutive samples, totaling 256 samples per batch. The constitution of these sequences is determined at the start of each epoch. For fine-tuning, we switch to the Adam optimizer with a step learning rate decay schedule. The specific
Training Strategy Details:
26

---

Table 14: SW-T Model Configurations.
Datasets
MOD
ACIDS
RealWorld-HAR
PAMAP2
Dropout Ratio
0.2
0.2
0.2
0.2
Patch Size
aud: [1, 40], sei: [1.1]
[1.
[1. 2]
[].
Window Size
[3, 3]
[2,4]
[3.
[3,5]
Block Numbers
[2. 2, 4]
[2. 2, 4]
[2. 2,
[2. 2, 2]
Block Channels
[64, 128, 256]
[64, 128, 256]
[32. 64, 128]
[32, 64, 128)
Head Num
4
4
4
4
Mod Fusion Channel
256
256
128
128
Mod Fusion Head Num
4
4
4
Mod Fusion Block
2
2
FC Dim
512
512
256
128
learning rates and epoch counts for pretraining vary by dataset as indicated in Table 13 chosen to ensure convergence. Hyperparameters for AdaTS's components, such the loss weights Aoe and A (Equation D and the dynamic temporal assignment temperature Ty, are tuned based on performance held-out validation set for the primary downstream task (e.g., classification accuracy). For UCR/UEA datasets, we adhere to the implementation and training configurations of TS2Vec [68] and 34 when integrating AdaTS.
SoftCLT
Implementation & Computation:
We develop the code from the open-source implementations
of foundational models and techniques 7 337 33 33) using PyTorch 2.0.1. We will release our
code upon acceptance. Evaluations are performed on NVIDIA RTX 6000 Ada GPUs with 48GB of memory. The training time varies from few minutes for fine-tuning to approximately one day for pretraining on the largest datasets with the SW-T backbone.
H Broader Impacts
This paper proposes AdaTS, an adaptive time-series representation learning framework that offers several positive societal impacts. By improving the accuracy and robustness of time-series analysis across diverse domains, AdaTS can enhance IoT applications in healthcare monitoring, industrial systems, and environmental sensing. The improved performance with limited labeled data makes machine learning more accessible for resource- conourreeen IoT settings where obtaining annotations is expensive or impractical. Additionally, AdaTS's s compute efficiency compared to traditional time- series similarity metrics promotes more environmentally sustainable Al applications by enabling effective learning without excessive computational requirements.
However, we acknowledge several potential negative implications. Enhanced time-series analysis capabilities could facilitate more pervasive monitoring in sensitive contexts like personal activity tracking, potentially compromising user privacy. To address these concerns, future research should focus on exploring human- sensing correlations and establishing comprehensive ethical guidelines for IoT applications, particularly in domains involving human activity recogmition. We acknowledge that our studies did not use real-world human subject data, and we are committed to ensuring that future research adheres to ethical standards and prioritizes user privacy.
As time-series sensing and computing become more ubiquitous, frameworks like AdaTSS that effi- ciently extract meaningful patterns from unlabeled data will play increasingly important role in shaping how we interact with and benefit from loT technologies.
27


Reviews:
[{"review_id": "MJrjyUxLjq", "reviewer": "Reviewer_6nSn", "summary": "This paper proposes AdaTS, an adaptive soft contrastive learning framework for time series representation learning. AdaTS introduces three core components:\n\n- Time-Frequency Coherence (TFC) – a spectral-domain similarity metric meant to reflect physics-guided relationships\n- Ordinal Consistency Learning (OCL) – a ranking-based loss that enforces relative similarity between samples rather than absolute distances\n- Dynamic Temporal Assignment (DTA) – a mechanism that assigns temporal similarity weights per sequence, based on a dissimilarity statistic.\n\nAdaTS is designed as a plug-and-play module that can be integrated into existing contrastive learning frameworks (e.g., TS2Vec, FOCAL, TS-TCC). The authors demonstrate consistent improvements in performance across a wide range of datasets (IoT, HAR, UCR/UEA), particularly under low-label regimes. Ablation studies and hyperparameter sensitivity analyses are also provided.", "questions": "Q1: How does TFC compare, both conceptually and empirically, with prior frequency-domain contrastive metrics like TF-C [1] and MF-CLR [2]? What are the unique advantages of your TFC integration?\n\nQ2: Have you considered learning the decay parameter κₙ via a small network or attention mechanism? Would this allow more flexible and robust adaptation than handcrafted statistics?\n\nQ3: Does ordinal consistency remain effective under noisy or highly dynamic conditions? Can you provide a robustness study or embedding analysis to support this?\n\nQ4: Can you include visualizations (e.g., t-SNE, PCA) of the learned embedding space with and without AdaTS to clarify how the representation quality improves?\n\nQ5: Are there scenarios where AdaTS fails or provides no improvement—especially on more stationary or synthetic datasets? Have you analyzed such cases?\n\nQ6: What physical phenomena or applications specifically benefit from the use of TFC? Can you show that TFC scores correlate with known physical behaviors (e.g., gait cycles, vibration modes)?", "limitations": "nil", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n- The paper addresses important challenges in contrastive learning for time series, especially in handling non-stationarity and noisy similarities.\n- The framework is modular and integrates easily with existing self-supervised learning (SSL) methods, offering strong performance gains with minimal overhead.\n- The proposed dynamic temporal assignment (DTA) is a practical and well-motivated solution to adapting similarity decay per sequence, and it is supported by reasonable empirical evidence.\n- The empirical improvements are consistent across different baselines and settings, especially in challenging, low-label regimes.\n\n## Weaknesses\n- The use of Time-Frequency Coherence (TFC) is adapted from classic signal processing literature [6], but lacks novelty. Related works like TF-C [1] and MF-CLR [2] have already explored frequency-domain similarity in contrastive learning. The contribution here appears more engineering-oriented than conceptual.\n- The Ordinal Consistency Learning (OCL) component is based on well-established triplet ranking loss formulations. It is not clearly distinguished from prior works such as SoftCLT [3] and TNC [4], and lacks qualitative justification (e.g., embedding smoothness or class structure).\n- The κₙ statistic used for temporal assignment is heuristically defined based on log similarity of adjacent time steps. This approach is sensitive to noise, and the paper does not investigate learning-based alternatives or validate the statistic's robustness (e.g., as explored in StatioCL [5]).\n- The evaluation is heavily focused on downstream accuracy, with no exploration of embedding space quality (e.g., t-SNE visualizations, clustering metrics, intra-class variance) to support the claim of improved representation.\n- The term “physics-guided” is frequently used but weakly justified. There are no domain-specific case studies or physical grounding (e.g., phase alignment, system resonance) that explain how TFC captures meaningful physical signal characteristics.\n- The gains on less dynamic datasets (UCR/UEA) are modest, raising the question of whether AdaTS consistently benefits static time series, and under what conditions it provides significant value.\n\n[1] Zhang, Xiang, et al. \"Self-supervised contrastive pre-training for time series via time-frequency consistency.\" NIPS, (2022): 3988-4003.\n\n[2] Duan, Jufang, et al. \"MF-CLR: Multi-frequency contrastive learning representation for time series.\" ICML, 2024.\n\n[3] Lee, Seunghan, Taeyoung Park, and Kibok Lee. \"Soft Contrastive Learning for Time Series.\", ICLR\n\n[4] Tonekaboni, Sana, Danny Eytan, and Anna Goldenberg. \"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding.\" ICLR, 2021\n\n[5] Wu, Yu, et al. \"StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal Contrast.\" ICKM, 2024.\n[6] Zhan, Yang, et al. \"Detecting time-dependent coherence between non-stationary electrophysiological signals—A combined statistical and time–frequency approach.\" Journal of neuroscience methods 156.1-2 (2006): 322-332.", "was_revised": true, "final_justification": "The authors addressed my concerns in full details", "rebuttal": "# Reviewer 6nSn\nWe sincerely appreciate the reviewer's constructive feedback and insights on our work. We hope to address your concerns below by clarifying the justification for individual components of AdaTS and providing additional experimental results to better understand AdaTS's performance.\n\n**Additional Analysis Summary:** We conducted extensive experiments addressing reviewer concerns and additional cross-reviewer analysis: (1) TF-C/MF-CLR comparison for performance validation, (2) Wavelet vs TFC analysis showing TFC superiority in general cases while wavelets demonstrate advantages in high-dynamics scenarios (9ssm response), (3) $\\kappa_n$ statistic design and robustness discussion, (4) Oversampling robustness experiments with distance regularization recovering 52% of performance loss under extreme imbalance (CHjp response), (5) T-SNE visualizations in Appendix D.2 Figure 6 showing improved cluster separation, and (6) Computational efficiency validation with minimal 3-7% overhead across different frameworks and sequence lengths (9ssm response).\n\n**1. Novelty and Positioning Relative to Prior Work** - [W1, W2, Q1]\n\nWhile we acknowledge the importance of frequency-domain analysis, AdaTS addresses fundamental contrastive learning limitations beyond similarity metric design:\n\n1. Unified Soft Assignment Framework: Unlike TF-C/MF-CLR, which focus solely on frequency-domain feature extraction of individual samples, AdaTS introduces a comprehensive soft assignment strategy that addresses both inter-sequence instance similarities and intra-sequence temporal dynamics simultaneously. Critically, existing frameworks treat all negative pairs equally, ignoring inherent correlations. AdaTS fundamentally changes this by adaptively weighting both temporal relationships within sequences and ordinal relationships across sequences. \n\n2. Empirical Superiority Over Recent Methods: During the rebuttal period, we conducted additional experiments comparing AdaTS with TF-C and MF-CLR. We present the results in Table below:\n\n|Model|ACIDS||MOD||PAMAP2||RWHAR||\n|-|-|-|-|-|-|-|-|-|\n||Acc|F1|Acc|F1|Acc|F1|Acc|F1|\n|TFC|0.7863|0.6448|0.5787|0.5712|0.6593|0.6058|0.7998|0.7049|\n|MF-CLR|0.8343|0.6587|0.8058|0.8042|0.7445|0.7045|0.7940|0.7954|\n|FOCAL|0.9347|0.8272|0.9548|0.9540|0.8438|0.8243|0.9261|0.9327|\n|FOCAL + AdaTS|**0.9571**|**0.8480**|**0.9705**|**0.9700**|**0.8648**|**0.8560**|**0.9527**|**0.9568**|\n\nWe will add these results to the revised version of the paper at Table 1.\n\n3. Novelty of Ordinal Consistency Loss (OCL): Current frameworks [64, 13, 35, 32] apply uniform temporal decay to all sequences. AdaTS introduces sequence-customized adaptive assignment via the $\\kappa_n$ statistic, dynamically adjusting weights based on each sequence's non-stationarity—a novel approach distinct from SoftCLT [32] and TNC [51].\n\nFurthermore, unlike triplet ranking, AdaTS's OCL addresses a critical challenge in dynamic time series: absolute similarity values become unreliable in high-bandwidth and non-stationary signals due to noise and temporal variations. AdaTS generates ordinal relationships across different sequences using frequency-domain analysis, preserving relative similarity orders rather than absolute values. This approach is inherently more robust because relative relations remain stable even when absolute similarity measurements cause misalignment due to environmental noise, signal dynamics, or varying recording conditions. By preventing representation degradation from inappropriately contrasting genuinely similar instances, AdaTS overcomes a key limitation evident in SoftCLT's poor performance on high-bandwidth signals (Table 1, ACIDS/MOD comparison). We will clarify these distinctions in the revised version of the paper.\n\n4. Plug-and-Play Generalizability and Efficiency: AdaTS functions as a flexible module compatible with multiple existing frameworks (TS2Vec [64], TS-TCC [13], FOCAL [35]) without requiring architectural changes, while accommodating various similarity metrics beyond time-frequency analysis (Table 5). The design with TFC achieves 40× speedup over past time-series similarity metrics (Table 3) and demonstrates strong generalizability across high-frequency domains (Table 1, ACIDS/MOD performance gains).\n\n**2. Technical Robustness and Design Choices** - [W3, Q2, Q3]\n\n1. $\\kappa_n$ Statistic Design and Robustness:\nOur design prioritizes computational efficiency and interpretability over learned alternatives. While attention mechanisms offer flexibility, they introduce significant pretraining overhead and overfitting risks.\nOur $\\kappa_n$ statistic provides an interpretable temporal unpredictability measurement with only $O(T)$ operations per sequence, adding merely 3-7\\% computational overhead (Table 2 and Q4 in 9ssm response).  The negative logarithm transformation inherently provides noise robustness by emphasizing larger drops over gradual variations, while batch normalization (Algorithm 3, line 10) stabilizes values across different sequences. Since $\\kappa_n$ measures average adjacent dissimilarity, it remains robust to isolated noisy samples that significantly affect learned parameters.\n\n2. Ordinal Consistency Under Severe Distribution Imbalance:\nWe conducted systematic evaluations demonstrating AdaTS's robustness under challenging conditions.\nControlled PAMAP2 experiments with extreme oversampling (up to 10x for frequent classes) showed that while vanilla AdaTS degrades under severe imbalance, distance regularization effectively mitigates embedding space collapse, maintaining robust performance even with 400\\% dataset size increases (Oversampling results table in CHjp response). \nAdaTS demonstrates particularly strong performance on high-bandwidth, non-stationary datasets (ACIDS, MOD) versus lower-dynamic datasets (HAR), indicating ordinal consistency excels precisely where absolute similarity measurements fail. Consistent improvements over SoftCLT on these challenging datasets (Table 1) validate our approach's robustness.\n\n3. Noise Robustness Validation: We conducted noise robustness experiments with Gaussian noise at multiple levels ($\\sigma$ = 0.1 to 1.0, relative to signal standard deviation), evaluating ordinal consistency effectiveness under corruption.\n\n|Dataset|$\\sigma$ = 0.1||$\\sigma$ = 0.2||$\\sigma$ = 0.5||$\\sigma$ = 1.0||\n|-|-|-|-|-|-|-|-|-|\n||FOCAL|+AdaTS|FOCAL|+AdaTS|FOCAL|+AdaTS|FOCAL|+AdaTS|\n|PAMAP2 Acc|0.8425|**0.8596**|0.8494|**0.8584**|0.8562|**0.8612**|0.8557|**0.8648**|\n|PAMAP2 F1|0.8302|**0.8488**|0.8370|**0.8493**|0.8464|**0.8508**|0.8448|**0.8587**|\n|ACIDS Acc|0.9338|**0.9479**|0.9429|**0.9520**|0.9365|**0.9384**|0.9146|**0.9374**|\n|ACIDS F1|0.7947|**0.8629**|0.8199|**0.8375**|0.7809|**0.8456**|0.7859|**0.8590**|\n\nAdaTS consistently outperforms FOCAL across all noise levels with up to +7.31\\% F1 improvement. While FOCAL's frequency-domain processing provides baseline robustness on PAMAP2 (where Gaussian noise acts as regularization for low-dynamic activities), AdaTS's temporal supervision becomes crucial for high-frequency ACIDS data. TFC's spectral coherence analysis filters noise while preserving signal correlations, and ordinal consistency maintains relative similarity relationships when absolute metrics fail under extreme noise corruption.\n\n**3. Representation Quality and Embedding Analysis** - [W4, Q4]\n\nWe appreciate the reviewer's concern about embedding space quality analysis. We would like to kindly refer the reviewer to Appendix D.2, Figure 6, which includes a comprehensive t-SNE analysis on representation quality. Figure 6 presents t-SNE visualizations comparing FOCAL baseline with AdaTS across all datasets, demonstrating clear qualitative improvements with more distinct clusters, better class separation, and improved cohesion across application domains. These qualitative improvements are also supported by the quantitative results in Table 1, where AdaTS consistently outperforms the baseline across all datasets.\n\n**4. Physical-Guided Term Clarification and Applications** - [W5, Q6]\n\nWe understand the reviewer's concern about our \"physics-guided\" terminology. While TFC captures frequency-domain relationships that reflect physical signal characteristics (e.g., harmonic content in vehicle engines, movement frequencies in human activities), we acknowledge the need for domain-specific validation. We will clarify the terminology to \"frequency-domain coherence analysis,\" noting that TFC's effectiveness stems from capturing spectral relationships that often correlate with underlying physical processes, thus providing a more accurate representation of our contribution's scope while specifying the need for deeper physical characterization in future work for specialized physical domains. \n\n**5. Method Limitations** - [W6, Q5]\n\n1. Performance Gradient Across Dataset Complexity: AdaTS consistently improves baselines across diverse signal characteristics, with performance gains correlating to dataset complexity. High non-stationarity datasets (ACIDS, MOD) show the most significant improvements where standard similarity metrics become unreliable, while moderate dynamics datasets (PAMAP2, HAR) demonstrate consistent gains as temporal relationships remain more predictable. Even stationary patterns (UCR/UEA) exhibit smaller but consistent improvements (Figure 2b), demonstrating robustness when adaptive mechanisms provide less advantage.\n\n2. Design Scope and Target Applications: AdaTS addresses fundamental challenges in unimodal and multimodal signals where standard similarity metrics become unreliable due to noise/dynamics, temporal relationships vary significantly across sequences, and cross-sequence instance similarities require robust handling. Performance gains correlate directly with signal complexity, demonstrating largest improvements on high-bandwidth datasets (ACIDS, MOD) where environmental noise and dynamics create the exact challenges our framework targets."}, {"review_id": "1758Afm7Om", "reviewer": "Reviewer_CHjp", "summary": "This paper presents a novel time series representation learning method through soft contrastive learning. The three major components of AdaTS are Time-Frequency Coherence (capturing harmonic structures and physical correlations), Ordinal Consistency Learning (avoiding noisy absolute similarity values and preserving semantic relationships across sequences), and Dynamic Temporal Assignment (adapting to varying non-stationarity). The final composite loss is designed as a linear combination of the above components.", "questions": "1. How can we prevent oversampling-induced shrinkage of embedding spaces in ordinal consistency learning? Could adaptive triplet mining or distance regularization help?\n2. Can wavelet transforms or Wigner-Ville distributions replace STFT in TFC to capture transient dynamics (e.g., mechanical faults or bio-signal spikes) without sacrificing efficiency?\n3. How to integrate cross-modal coherence (e.g., joint TFC for acoustic-seismic pairs) to leverage complementary information while preserving modality-specific features?\n4. Can the temporal unpredictability metric be computed incrementally for streaming data, enabling online adaptation to sudden signal changes?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "The major strengths of this paper:\n\n1. **Performance Gains**: \n\t- The experiment results demonstrate a prominent accuracy boost compared to sota benchmarks in Table 1, indicating that the proposed method is effective on benchmark datasets.\n2. **Efficiency & Adaptability**:\n\t- TFC reduces memory by **2×** vs. DTW with minimal runtime overhead (Table 3).\n\t- DTA dynamically handles diverse non-stationarity (e.g., abrupt vs. smooth sequences).\n3. **Generalizability**:\n\t- Pluggable into SOTA frameworks (TS2Vec, TS-TCC, FOCAL), improving all (Table 1).\n\t- Consistent gains on UCR/UEA benchmarks (Figure 2b).\n\nThe major weakness of this paper:\n\n1. **Oversampling in Ordinal Learning**:\n\t- Frequent sampling of highly similar instances may shrink embedding space diversity.\n2. **TFC’s Stationarity Assumption**:\n\t- STFT assumes local stationarity, limiting performance on highly non-stationary signals.\n3. **Parameter Sensitivity**:\n\t- Margin *δ* (ordinal loss) and temperature $\\tau_T$ (DTA) require per-dataset tuning (Figure 5).", "was_revised": false, "final_justification": "", "rebuttal": "# Reviewer CHjp\n\nWe appreciate the reviewer's thoughtful and detailed comments. Below, we address each concern and provide additional experimental results.\n\n**Additional Analysis Summary:** We conducted extensive experiments addressing reviewer concerns and additional cross-reviewer validation: (1) Controlled oversampling experiments on PAMAP2 (up to 10× for frequent classes) with distance regularization recovering 52% of performance loss, (2) Wavelet vs STFT comparison showing TFC superiority in most scenarios while wavelets showing advantages in high-dynamics cases (9ssm response), (3) Incremental κₙ formulation for streaming data with O(1) complexity per sample, potentially enabling real-time deployment, (4) Cross-modal coherence formulation for acoustic-seismic pairs capturing phase relationships, and (5) Parameter sensitivity analysis demonstrating that default settings (λₒc ∈ [0.6, 0.8], τₜ ∈ [0.1, 0.3]) achieve near-optimal performance across datasets.\n\n**1. Ordinal Learning and Embedding Space Preservation** - [W1, Q1]\n\nThank you for this important question about embedding space preservation under oversampling conditions. We further investigated this concern through controlled experiments on PAMAP2, systematically oversampling the three most frequent classes (walking, ironing, lying) at ratios of 1×, 2×, 5×, and 10×, resulting in dataset size increases of 35%, 140%, and 315% respectively.\n\n**Impact of Oversampling on AdaTS Performance (PAMAP2 Dataset)**\n\n|Method|1× (Baseline)||2× (+35%)||5× (+140%)||10× (+315%)||\n|--|--|--|--|--|--|--|--|--|\n||Acc|F1|Acc|F1|Acc|F1|Acc|F1|\n|FOCAL|0.8427|0.8258|0.8378|0.8318|0.8200|0.8154|0.8063|0.8014|\n|FOCAL + AdaTS|0.8637|0.8535|0.8422|0.8331|0.8154|0.8039|0.8016|0.7985|\n|FOCAL + AdaTS + DistReg|**0.8639**|**0.8510**|**0.8504**|**0.8424**|**0.8385**|**0.8362**|**0.8339**|**0.8241**|\n\nWe observe that extreme cases of oversampling led to significant performance degradation in both AdaTS and FOCAL due to embedding space shrinkage. Specifically, at 10× oversampling, vanilla AdaTS accuracy drops from 0.8637 to 0.8016 (-6.21%), while FOCAL drops from 0.8427 to 0.8063 (-3.64%).\n\nTo mitigate this issue, we experimented with cosine-based distance regularization following the anti-collapse framework of Bardes et al. (VICReg, ICLR 2022). The results demonstrate significant recovery: FOCAL + AdaTS + DistReg maintains 0.8339 accuracy even at 10× oversampling, recovering 52% of the performance loss compared to vanilla AdaTS. This validates the effectiveness of distance regularization for preventing embedding space collapse under extreme imbalance conditions. We recommend applying distance regularization when dataset imbalance exceeds 5× oversampling ratios or when embedding space diversity becomes a concern. We will add this analysis to the revised version of the paper.\n\n**2. Time-Frequency Transform Limitations for Non-Stationary Signals** - [W2, Q2]\n\nAs suggested by the reviewers, we implemented **CWT using Morlet wavelets** as a direct replacement for STFT in our TFC similarity computation. We evaluated both approaches across all datasets using identical experimental conditions and hyperparameters. The results presented in the following table show that while wavelets demonstrate competitive performance and theoretical advantages for capturing transient phenomena, STFT-based TFC maintains superior overall performance across most of our datasets.\n\n**Performance Comparison: Wavelet Transform vs. STFT-based TFC**\n\n|Method|ACIDS||PAMAP2||RealWorld-HAR||MOD||\n|--|--|--|--|--|--|--|--|--|\n||Acc|F1|Acc|F1|Acc|F1|Acc|F1|\n|Wavelet AdaTS|0.9525|0.8346|0.8438|0.8353|**0.9533**|**0.9577**|0.9511|0.9496|\n|TFC AdaTS|**0.9571**|**0.8480**|**0.8648**|**0.8560**|0.9527|0.9568|**0.9705**|**0.9700**|\n\nTFC outperforms wavelets on most datasets (ACIDS: +1.78%, MOD: +1.94%, PAMAP2: +2.10%), while wavelets show advantages in specific high-dynamics scenarios. Our detailed analysis in the 9ssm response confirms this pattern, with wavelets showing advantages in highly agile scenarios (Type 7 vehicles: +17.4% advantage) while TFC consistently outperforms in steady patterns and generalizes better across datasets.\n\n**3. Parameter Sensitivity and Tuning Requirements** - [W3]\n\nWe appreciate the reviewer's observation about parameter sensitivity requirements. Time-series applications are inherently heterogeneous with diverse physical properties and temporal dynamics, naturally requiring some hyperparameter adjustment like other machine learning approaches. AdaTS maintains its plug-and-play generalizability while allowing parameter tuning for optimal domain-specific performance.\n\nFigure 5 demonstrates that AdaTS maintains robustness across reasonable parameter ranges without requiring precise fine-tuning. While dataset-specific configuration may improve performance, AdaTS does not require extensive hyperparameter search or exhibit extreme sensitivity that would make it impractical for deployment. The framework provides robust default settings (λₒc ∈ [0.6, 0.8], τₜ ∈ [0.1, 0.3]) achieving near-optimal performance across diverse applications.\n\n**4. Cross-Modal Extensions** - [Q3]\n\nThank you for your insightful question about leveraging cross-modal complementarity. AdaTS currently calculates temporal assignments for each modality independently and performs alignment after modality fusion. This is because different modalities exhibit distinct non-stationarity ranges, making their temporal assignments incomparable. Post-fusion alignment allows consistent temporal assignment across modalities while training their encoders accordingly.\n\nFor acoustic-seismic pairs, cross-modal TFC can capture phase relationships between modalities—for instance, engine harmonics manifesting simultaneously in both acoustic recordings and ground vibrations from the same vehicle source. However, for our current implementation, such phase alignment is not required since the same events manifest in the same window without delay across modalities, though with different non-stationarity characteristics leading to different similarity characteristics. Instead, we capture temporal similarity relationships in a modality-consistent manner, then perform post-fusion alignment. If required, our TFC framework can be easily extended to compute cross-spectral density between modalities:\n\n$$\\text{CrossTFC}_{ij}^{(m_1,m_2)} = \\frac{1}{T \\cdot F} \\sum_{t,f} \\frac{|S_{ij}^{(m_1,m_2)}(t,f)|^2}{\\sqrt{S_{ii}^{(m_1)}(t,f) \\cdot S_{jj}^{(m_2)}(t,f)}}$$\n\nwhere $S_{ij}^{(m_1,m_2)}(t,f) = X_i^{(m_1)}(t,f) \\cdot X_j^{(m_2)*}(t,f)$ represents cross-spectral density between modalities $m_1$ and $m_2$. This extension captures phase relationships between modalities (e.g., engine harmonics in both acoustic and seismic signals) and could benefit applications requiring precise cross-modal synchronization.\n\n**5. Streaming Adaptations** - [Q4]\n\nThank you for this insightful question about extending AdaTS to streaming data. Yes, κₙ can be computed incrementally using sliding windows that maintain rolling averages of adjacent dissimilarities. As new samples arrive, we can simply update κₙ by adding new dissimilarity measures and removing the oldest ones, achieving O(1) computational complexity per sample:\n\n$$\\kappa_n^{(t+1)} = \\kappa_n^{(t)} + \\frac{1}{W}[-\\log(D^T_n[t+1, t] + \\epsilon) + \\log(D^T_n[t-W+1, t-W] + \\epsilon)]$$\n\nwhere $W$ is the sliding window size and $D^T_n$ represents the temporal similarity matrix for sequence $n$.\n\nThis incremental computation benefits AdaTS deployment in real-time scenarios. For example, activity recognition systems can immediately adapt when users transition from stationary (e.g., sitting) to dynamic activities (e.g., running), while industrial monitoring can detect machinery state changes through sudden κₙ increases for predictive maintenance. The key advantage is enabling edge IoT deployment where batch preprocessing is impractical, making our framework suitable for responsive applications where signal non-stationarity changes unpredictably. We will add this discussion to the revised version of the paper."}, {"review_id": "QrHc4jMPn6", "reviewer": "Reviewer_RtuZ", "summary": "This paper proposes a method for adaptive time series representation learning using contrastive learning. It introduces three components: a physics-guided time-frequency similarity measure, ordinal consistency learning, and dynamic temporal assignment. The goal is to improve representation quality in the presence of non-stationarity and unclear similarity. Experiments show AdaTS enhances performance when integrated with existing contrastive learning methods.", "questions": "1. Can the authors clarify why Time-Frequency Coherence is an appropriate choice for the datasets used? In what scenarios is this metric applicable or not applicable? Are there cases where it might fail or mislead similarity estimation?\n\n2. What is the downstream task being evaluated—full sequence classification, segment-level prediction, or pointwise classification? Please make this clear in the main text.\n\n4. Can the authors include more recent and competitive baselines? This would help position AdaTS relative to the SOTA.\n\n5. Are there results of testing AdaTS on open time series datasets or other benchmark collections? That would enhance reproducibility and comparability.\n\n6. Please revise Figure 1 with more descriptive labeling and stepwise explanation in the caption or main text to guide the reader through the proposed pipeline.", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper provides a detailed discussion of the limitations of existing contrastive time series methods and motivates the need for adaptivity. This is very good.\n2. The design of the Dynamic Temporal Assignment module is interesting and reasonably novel.\n3. Writing is rich and technically detailed, showing clear effort in execution and presentation.\n\nWeaknesses:\n1. Main concern: The Physics-Guided Time-Frequency Similarity module appears to reuse an existing method and without clear justification for its applicability in the proposed setting.\n2. The problem definition is vague—it is not clearly stated what the actual task is (e.g., classification over full sequences, segments, or time points), which makes it hard to interpret the results.\n3. The experimental baselines are not sufficiently up-to-date, with most comparisons ending at 2023. Several more recent 2024–2025 baselines exist in the time series contrastive learning literature.\n4. The paper would benefit from evaluations on open public time series datasets to better contextualize the results and benchmark generalizability.\n5. Figure 1 is difficult to interpret without sufficient explanation, especially for readers unfamiliar with the overall flow. A step-by-step narrative would help.\n6. Minor formatting issues exist—for example, unclear spacing between the main text and footnotes on the first page.", "was_revised": true, "final_justification": "Thanks to the authors for the detailed rebuttal and the extra experiments. The additional baselines, the clearer explanation of setups, and the new large results on public datasets all strengthen the paper. I also appreciate the clarification on why TFC was chosen and where it might have limitations.\nThat said, my main reservations are only partly addressed. The TFC component still reads as a reuse of an existing technique, and I’m not fully convinced that its necessity over other reasonable alternatives has been demonstrated. While the expanded evaluation is helpful, it doesn’t fully change my view on the conceptual novelty or the overall impact of the work. I see the value in the proposed adaptivity, but I’m keeping my score given these remaining concerns.", "rebuttal": "# Reviewer RtuZ\n\nWe sincerely appreciate the reviewer’s constructive feedback and insights on our work. We hope to address your concerns below by clarifying the justification for individual components of AdaTS, providing additional experimental results, and improving the clarity of our presentation.\n\n**Additional Analysis Summary:** We conducted extensive experiments addressing reviewer concerns and additional cross-reviewer validation including: (1) Additional baseline evaluation with recent 2024 methods SoftCLT (existing, ICLR 2024), MF-CLR (ICML 2024) and competitive baselines TF-C (NeurIPS 2022), Informer (AAAI 2021), LIMU-BERT (SenSys 2021) addressing state-of-the-art comparison gaps, (2) Dataset accessibility validation confirming majority are open-source (MOD, RWHAR, PAMAP2, UCR, UEA), (3) Systematic wavelet vs TFC comparison demonstrating TFC superiority in most scenarios (9ssm response), (4) Oversampling robustness analysis with extreme class imbalance mitigation (CHjp response), and (5) Computational efficiency and sequence length validation showing 3-7% overhead (9ssm response).\n\n**1. Time-Frequency Coherence Justification** - [W1, Q1]\n\nThank you for this important question regarding the justification and applicability of Time-Frequency Coherence (TFC) in our framework. While TFC builds upon established signal processing principles, our contribution lies in its novel integration within contrastive learning for time series representation. In this work, we first identified the imperfections of existing similarity metrics, such as cosine similarity, commonly used in standard contrastive learning, and dynamic time warping (DTW) used in SoftCLT. Our justification for selecting TFC as the similarity metric in contrastive learning can be summarized by two motivations:\n\n**1.1 Efficiency**: As we have discussed in the introduction, existing DTW-variants (DTW, FastDTW) are computationally expensive and impractical for iterative training on large-scale datasets. TFC addresses this limitation through FFT-based operations, achieving over 40× speedup compared to DTW (Table 3) while maintaining comparable computational overhead to simple metrics like Euclidean and Cosine similarity. Our detailed computational analysis (9ssm response) shows TFC adds only 3-7% overhead across different frameworks and sequence lengths, making it practically viable for large-scale deployment.\n\n**1.2 Effectiveness**: Another challenge in time-series representation learning is capturing the inherent physical correlations. Many standard similarity methods (e.g., Cosine, Euclidean) overlook underlying physical signal properties and are susceptible to noise. TFC provides a physically meaningful measure by capturing harmonic structures and spectral relationships that reflect underlying physical processes, enabling robust similarity estimation across varying conditions without introducing non-linear temporal deformations that can distort signal characteristics. Our ablation studies comparing TFC with other metrics in Table 5 further validate its effectiveness.\n\nHowever, we do understand the potential limitations of TFC in terms of applicability. Although AdaTS with TFC as the similarity metric has already consistently excelled across the datasets used, TFC may underperform under extreme scenarios where time-series events shift instantaneously, where wavelets may provide advantages for highly agile, rapid maneuvers (wavelet case studies in 9ssm response). In the revised manuscript, we will expand the limitation section to discuss potential failure modes of AdaTS.\n\n**2. Problem Definition and Task Clarity** - [W2, Q2]\n\nOur evaluation primarily focuses on **segment-level classification**, where each sample represents a fixed-length segment extracted from continuous time-series recordings. We segment the raw time-series data using sliding windows of predetermined window sizes based on the characteristics and temporal dynamics of each dataset. The following table provides detailed specifications of our evaluated datasets:\n\n|Dataset|Modalities (Freq)               |Sample Length|Overlap|#Labels|Downstream Task                                 |\n|-------|--------------------------------|-------------|-------|-------|------------------------------------------------|\n|ACIDS  |acoustic, seismic (all 1025Hz)  |1 sec        |50%    |27,595 |Vehicle Classification (9 classes)              |\n|MOD    |acoustic (8kHz), seismic (100Hz)|2 sec        |0%     |7,335  |Multi-task: Vehicle (7), Distance (4), Speed (4)|\n|PAMAP2 |acc, gyro, mag, lig (all 50Hz)  |5 sec        |50%    |9,611  |Activity Classification (18 classes)            |\n|RWHAR  |acc, gyr, mag (all 100Hz)       |2 sec        |50%    |12,887 |Activity Classification (8 classes)             |\n\nEach segment serves as an independent sample for pretraining and classification, with segment lengths chosen to capture meaningful temporal patterns. We will clarify this segmentation approach and expand preprocessing details in Appendix A of the revised manuscript for better reproducibility.\n\nAdditionally, we comprehensively evaluate on UCR/UEA archives:\n\n|Archive           |#Datasets|Total Samples|Avg Length|Domains                 |\n|------------------|---------|-------------|----------|------------------------|\n|UCR (univariate)  |125      |166,500+     |50-2,844  |Medical, Motion, Sensor |\n|UEA (multivariate)|29       |47,000+      |8-5,730   |HAR, Medical, Industrial|\n\nThese datasets exhibit more stationary patterns compared to our primary IoT datasets, yet AdaTS demonstrates consistent improvements (Figure 2b), validating its robustness across diverse temporal dynamics.\n\n**3. Experimental Evaluation and Baseline Coverage** - [W3, Q3]\n\nWe appreciate the reviewer’s concern regarding the comprehensiveness of our baseline comparisons. We acknowledge that including more recent state-of-the-art methods would strengthen our evaluation and better position AdaTS within the current landscape.\n\nOur current evaluation includes SoftCLT (ICLR 2024) as a recent contrastive learning baseline, along with established methods such as TNC, TS2Vec, and FOCAL from 2021-2023 that remain widely adopted in the time series representation learning community. Due to the rebuttal timeline constraints, we have conducted additional experiments with MF-CLR (ICML 2024), TF-C (NeurIPS 2022), and incorporated comparisons with Informer (AAAI 2021) and LIMU-BERT (SenSys 2021).\n\n|Model        |ACIDS               ||MOD                 ||PAMAP2              ||RWHAR               ||\n|-------------|----------|----------|----------|----------|----------|----------|----------|----------|\n||Acc       |F1        |Acc       |F1        |Acc       |F1        |Acc       |F1        |\n|Informer     |0.9470    |0.8455    |0.8972    |0.8961    |**0.8746**|**0.8660**|0.9313    |0.9353    |\n|LIMU-BERT    |0.5556    |0.3712    |0.4297    |0.3970    |0.7781    |0.7554    |0.8120    |0.7508    |\n|TFC          |0.7863    |0.6448    |0.5787    |0.5712    |0.6593    |0.6058    |0.7998    |0.7049    |\n|MF-CLR       |0.8343    |0.6587    |0.8058    |0.8042    |0.7445    |0.7045    |0.7940    |0.7954    |\n|FOCAL        |0.9347    |0.8272    |0.9548    |0.9540    |0.8438    |0.8243    |0.9261    |0.9327    |\n|FOCAL + AdaTS|**0.9571**|**0.8480**|**0.9705**|**0.9700**|0.8648    |0.8560    |**0.9527**|**0.9568**|\n\nWe hope these additional comparisons can provide a more complete assessment of AdaTS’s contributions and help better understand its relative performance as a pluggable module in existing time-series contrastive learning frameworks.\n\n**4. Open public time-series datasets** - [W4, Q4]\n\nWe appreciate the reviewer’s emphasis on using open public datasets for reproducibility and benchmarking. All our primary datasets are publicly available: PAMAP2[1], RWHAR[2], MOD[3], UCR[4], and UEA[5]. As shown in the table above, our comprehensive evaluation includes 154 public datasets from UCR/UEA archives, covering diverse domains from medical to industrial applications. This extensive benchmarking on standardized public datasets helps ensure reproducibility and demonstrates AdaTS’s generalizability across varied time series characteristics.\n\n[1] Attila Reiss and Didier Stricker. Introducing a new benchmarked dataset for activity monitoring. In 2012, the 16th International Symposium on Wearable Computers, pages 108–109. IEEE, 2012.\n\n[2] Timo Sztyler and Heiner Stuckenschmidt. On-body localization of wearable devices: An investigation of position-aware activity recognition. In 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), pages 1–9. IEEE, 2016.\n\n[3] Shengzhong Liu, et al. “Focal: contrastive learning for multimodal time-series sensing signals in factorized orthogonal latent space.” In Proceedings of the 37th International Conference on Neural Information Processing Systems, 2023.\n\n[4] Hoang Anh Dau, et al. “The ucr time series archive”. IEEE/CAA Journal of Automatica Sinica, 2019.\n\n[5] Anthony Bagnall, et al. “The uea multivariate time series classification archive”, 2018.\n\n**5. Presentation and Figure Clarity** - [W5, Q5, W6]\n\nWe thank the reviewer for this valuable feedback regarding the clarity of our main overview figure. We acknowledge that the current Figure 1 contains extensive technical details that may hinder interpretation, particularly for readers unfamiliar with the overall AdaTS framework.\n\nIn the revised manuscript, we will:\n\n- Create a cleaner visualization with clearly labeled components and numbered processing steps (with circled number markers)\n- Include a step-by-step walkthrough of the pipeline from input to final contrastive loss\n- Fix the formatting issues (text-footnote spacing) identified by the reviewer\n\nWe hope these revisions will ensure the figure provides clear guidance for readers from any background and address the reviewer’s concern regarding the interpretability of the figure."}, {"review_id": "Q1pYNyQgjg", "reviewer": "Reviewer_N6z2", "summary": "This paper claims that existing contrastive learning methods struggle with defining meaningful similarities and overlook inherent physical correlation and sequence-varying non-stationarity.\nTo improve the representational quality and real-world adaptability, this paper introduce a soft contrastive strategy, AdaTS, which uses frequency coherence for similarity measurement. Based on this, AdaTS proposes ordinal consistency learning for improving relative instance similarity and adapting to sequence non-stationarity. Empirical experiments demonstrate the effectiveness and flexibility of the proposed method.", "questions": "NA", "limitations": "NA", "rating": 4, "confidence": 1, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n- The experimental validation is comprehensive, including multiple datasets and several state-of-the-art baseline frameworks. The results demonstrate consistent performance improvements.\n- AdaTS is designed as a pluggable and flexile module. \n- Ablation study is provided for the analysis of the contribution of each component.\n\nWeakness:\n- In terms of the problem formulation, this papers claims the imperfection of existing similarity metrics. I think real-world examples should be provided to show the problem of existing metrics. That will strengthen the motivation of this study.", "was_revised": true, "final_justification": "The authors provided more justification of the problem formulation. I will keep the score unchanged, boardline accept.", "rebuttal": "# Reviewer N6z2\nWe appreciate the reviewer's constructive feedback on strengthening our problem motivation through concrete examples.\n\n**Additional Analysis Summary:** We conducted extensive analysis addressing reviewer concerns and additional cross-reviewer validation to strengthen problem motivation through concrete examples: (1) Real-world IoT deployment scenarios demonstrating computational and performance limitations of existing similarity metrics, (2) Practical timing constraints showing DTW's 44× computational overhead rendering it infeasible for real-time applications, (3) Security application examples where 6% accuracy gaps translate to critical misclassification rates, and (4) Healthcare monitoring scenarios where precise activity classification impacts patient safety, all supported by experimental evidence in Tables 3 and 5.\n\n**1. Problem Formulation and Real-World Examples** - [W1]\n\nThank you for this important observation. We provide concrete real-world examples demonstrating how existing similarity metrics fail in practical IoT deployments, supported by our experimental evidence in Tables 3 and 5.\n\n**Real-World Deployment Challenges:** Consider an IoT sensor network monitoring vehicle movements using acoustic and seismic sensors at 1025 Hz sampling rate. In such deployments:\n\n**1. Computational Infeasibility:** DTW-based similarity computation requires 10.895 seconds per comparison vs. TFC's 0.245 seconds (44× difference, Table 3). For real-time vehicle classification with hundreds of sensor readings per minute, this renders DTW impractical—a system processing 100 vehicle detections would require over 18 minutes using DTW vs. 0.4 minutes with TFC.\n\n**2. Performance Degradation in Noisy Environments:** In real deployments with environmental noise, wind interference, and varying terrain conditions, conventional metrics fail to capture meaningful similarities. Table 5 shows DTW achieves only 0.7837 accuracy on ACIDS compared to TFC's 0.8379 accuracy. This 6% performance gap translates to significant misclassification rates in security applications where distinguishing between civilian and military vehicles is critical.\n\n**3. Multi-Modal IoT Applications:** In human activity recognition systems using wearable sensors (accelerometers, gyroscopes at 50-100 Hz), existing metrics like Euclidean and Cosine similarity fail to capture the complex temporal patterns of activities like walking vs. running. Our PAMAP2 results demonstrate TFC's 0.7772 accuracy vs. DTW's 0.7575 accuracy, crucial for healthcare monitoring where accurate activity classification impacts patient safety.\n\nThese real-world constraints—computational limitations in edge devices, noisy sensor environments, and multi-modal signal complexity—demonstrate why existing similarity metrics are inadequate for modern IoT applications, validating AdaTS's design for practical deployment scenarios."}, {"review_id": "0A7vIbdNEs", "reviewer": "Reviewer_9ssm", "summary": "This paper introduces AdaTS, an adaptive soft contrastive learning method for time-series data. AdaTS introduces three novel components: a physics-guided similarity measure using Time-Frequency Coherence (TFC), which improves the robustness of time-series comparisons; an ordinal consistency learning strategy that preserves the relative ordering of similarities between instances, making it resilient to noisy data; and a dynamic temporal assignment mechanism that adapts to the varying dynamics of different sequences. AdaTS demonstrates improvements in the performance of self-supervised contrastive learning across various time-series datasets.", "questions": "1. The discussion briefly mentions an \"oversampling\" issue in the ordinal consistency loss. Can the authors elaborate on how significant this problem is, and whether any mitigation strategies were considered? How does the current framework handle imbalanced sampling?\n2. Although AdaTS is said to have minimal computational overhead during pretraining, how does this scale with increasing dataset size or sequence length?\n3. Validate TFC against wavelet-based coherence metrics on datasets with extreme non-stationarity (e.g., ACIDS during terrain transitions). Ablation study comparing STFT vs. wavelet transforms would clarify the approach’s limits and potential improvements.\n4. In multi-modal datasets (e.g., MOD with acoustic and seismic signals), each modality may exhibit distinct non-stationarity. Does AdaTS currently align modality-specific temporal assignments, or are they treated uniformly? How could cross-modality coherence enhance dynamic weighting?\n5. The method relies on two key hyperparameters, λ_oc and λ_t. How sensitive is the performance to the choice of these values across datasets?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. AdaTS leverages Time-Frequency Coherence to capture physical correlations in time series, addressing a critical gap in prior work that overlooked underlying signal physics. By analyzing harmonic structures in the frequency domain, TFC enables robust similarity quantification that is both computationally efficient and noise-resilient.\n2. The dynamic temporal assignment mechanism in AdaTS adapts to sequence-specific non-stationarity by measuring each sequence’s temporal unpredictability. The ordinal consistency loss further reinforces relative similarity ordering, making representations more robust to noise and context-dependency.\n3. AdaTS is designed as a pluggable module, seamlessly integrating with three state-of-the-art contrastive frameworks. The extensive evaluations across 6 datasets demonstrate accuracy improvements, with rigorous ablation studies validating the contribution of each component. The low computational overhead highlights its practical applicability.\n\nWeaknesses:\n1. While TFC is effective for many signals, its reliance on short-time Fourier transform (STFT) assumes local stationarity, which may struggle with highly non-stationary data (e.g., abrupt transients). The framework could benefit from more adaptive time-frequency transforms (e.g., wavelets) to handle such cases, as acknowledged in the future work.\n2. In datasets with extreme dynamics (e.g., sudden environmental shifts), the dynamic temporal assignment’s reliance on average dissimilarity might fail to capture instantaneous changes. Incorporating real-time anomaly detection or event-driven weighting could enhance adaptability in such cases. \n3. The ordinal consistency loss may suffer from \"oversampling\" if similar instances are frequently sampled, potentially shrinking embedding distances. Although not observed in experiments, this issue requires mitigation strategies, such as adaptive sampling weights, to prevent representation collapse.\n4. While the method is effective, the additional complexity introduced by dynamic temporal assignments and the physics-guided TFC measure may require careful tuning, which could be an obstacle for practitioners seeking simplicity.", "was_revised": true, "final_justification": "Thank you for the authors' reply, which has addressed my question. I will maintain my original score for this work.", "rebuttal": "# Reviewer 9ssm\n\nWe sincerely appreciate the reviewer's thorough analysis and constructive suggestions. We hope to address your concerns below by clarifying AdaTS's technical limitations and providing additional experimental analysis to better understand its robustness and applicability.\n\n**Additional Analysis Summary:** We conducted extensive experiments addressing reviewer concerns and additional cross-reviewer validation: (1) Comprehensive wavelet vs STFT comparison on extreme non-stationarity scenarios showing complementary strengths (W1, Q3), (2) Oversampling robustness experiments with distance regularization preventing embedding collapse (W3, Q1), (3) Computational scalability analysis across different sequence lengths showing consistent 3-7% overhead (W4, Q2), (4) Multi-modal temporal alignment discussion and future directions (Q4), (5) Hyperparameter sensitivity validation demonstrating robust performance ranges (Q5), and (6) Comprehensive noise robustness validation confirming TFC's spectral filtering advantages and ordinal consistency effectiveness under noise corruption (6nSn response).\n\n**1. Time-Frequency Transform Limitations and Non-Stationarity** - [W1, Q3]\n\nThank you for this insightful concern about TFC's STFT-based limitations. As suggested by the reviewer, we implemented **CWT using Morlet wavelets** as a direct replacement for STFT in our TFC similarity computation. We evaluated both approaches across all datasets using identical experimental conditions and hyperparameters. The results show that while wavelets demonstrate competitive performance and theoretical advantages for capturing transient phenomena, STFT-based TFC maintains superior overall performance across most datasets.\n\n**Performance Comparison: Wavelet Transform vs. STFT-based TFC**\n\n|Method|ACIDS||PAMAP2||RealWorld-HAR||MOD||\n|--|--|--|--|--|--|--|--|--|\n||Acc|F1|Acc|F1|Acc|F1|Acc|F1|\n|Wavelet AdaTS|0.9525|0.8346|0.8438|0.8353|**0.9533**|**0.9577**|0.9511|0.9496|\n|TFC AdaTS|**0.9571**|**0.8480**|**0.8648**|**0.8560**|0.9527|0.9568|**0.9705**|**0.9700**|\n\n**1.1 ACIDS Case Study:** To understand complementary strengths of both approaches, we conducted additional analyses on classes with varying non-stationarity and dynamics. We categorized ACIDS vehicles by movement dynamics for systematic analysis across temporal and movement-based characteristics.\n\nOur analysis reveals that TFC demonstrates consistent advantages in the largest, most representative categories. TFC outperforms wavelets in heavy vehicles (937 samples, +1.9% advantage) and medium vehicles (313 samples, +0.9% advantage), while wavelets show advantages primarily in light vehicles (104 samples, +3.8% advantage).\n\n**ACIDS Category Classification Results**\n\n|Category|Types|Wavelet Acc|TFC Acc|Rationale|# Samples|\n|--|--|--|--|--|--|\n|Heavy vehicles|1,2,8,9|94.7%|**96.6%**|Steady movement, consistent patterns|937|\n|Medium vehicles|3, 5|81.8%|**82.7%**|Moderate but predictable dynamics|313|\n|Light vehicles|6, 7|**91.3%**|87.5%|Agile maneuvers, rapid acceleration|104|\n\nOur individual vehicle type analysis reveals that TFC advantages occur consistently across the largest sample sizes. TFC demonstrates superior performance in Type 2 (326 samples, +5.6% advantage), Type 8 (134 samples, +2.3% advantage), and Type 9 (179 samples, +2.7% advantage). Notably, the largest wavelet advantage (+17.4% for Type 7) occurs in the smallest sample size (23 samples) with the lightest vehicle type, indicating a specialized rather than generalizable advantage.\n\n**Individual Vehicle Type Performance**\n\n|Type|Category|Wavelet Acc|TFC Acc|Best|Advantage|Samples|Rationale|\n|--|--|--|--|--|--|--|--|\n|Type 2|Heavy vehicles|92.9%|**98.5%**|**TFC**|+5.6%|326|Consistent patterns favor coherence|\n|Type 9|Heavy vehicles|93.9%|**96.6%**|**TFC**|+2.7%|179|Consistent low-frequency signatures|\n|Type 8|Heavy vehicles|97.0%|**99.3%**|**TFC**|+2.3%|134|Steady, low-dynamics movement|\n|Type 7|Light vehicles|**60.9%**|43.5%|**Wavelet**|+17.4%|23|Highly agile, rapid maneuvers|\n\nIn summary, our findings reveal systematic complementary strengths that can enhance AdaTS: (1) Wavelets can provide advantages in highly dynamic scenarios or with abrupt changes, while (2) TFC provides a more generalizable performance across general cases and steady patterns, validated across larger sample sizes. *Importantly, AdaTS can leverage both approaches as a unified framework to improve performance and robustness across a variety of scenarios and tasks by selecting the most appropriate time-frequency transform based on signal dynamics.*\n\n**2. Dynamic Temporal Assignment Robustness** - [W2]\n\nWe understand the reviewer's concern regarding AdaTS's potential limitations in handling extreme instantaneous dynamics. While our current approach demonstrates robustness to gradual non-stationarity in dynamic datasets like ACIDS and MOD, we acknowledge that extreme instantaneous changes represent a more challenging edge case that our current framework may not fully address.\n\nDue to rebuttal timeline constraints, we could not conduct comprehensive experiments on extreme instantaneous dynamics or implement the sophisticated real-time anomaly detection mechanisms suggested by the reviewer. However, we recognize this as an important limitation and will include detailed discussion of these limitations and the reviewer's suggested solutions in the revised manuscript.\n\n**3. Ordinal Consistency Loss and Sampling Issues** - [W3, Q1]\n\nThank you for this important question about embedding space preservation under oversampling conditions. We investigated this concern through controlled experiments on PAMAP2, systematically oversampling similar instances from the three most frequent classes (walking, ironing, lying) across different sampling ratios (1×, 2×, 5×, and 10×), resulting in overall dataset size increases of 35% to 400%.\n\n**Impact of Oversampling on AdaTS Performance (PAMAP2 Dataset)**\n\n|Method|1× (Baseline)||2× (+35%)||5×||10×||\n|--|--|--|--|--|--|--|--|--|\n||Acc|F1|Acc|F1|Acc|F1|Acc|F1|\n|FOCAL|0.8427|0.8258|0.8378|0.8318|0.8200|0.8154|0.8063|0.8014|\n|FOCAL + AdaTS|0.8637|0.8535|0.8422|0.8331|0.8154|0.8039|0.8016|0.7985|\n|FOCAL + AdaTS + DistReg|**0.8639**|**0.8510**|**0.8504**|**0.8424**|**0.8385**|**0.8362**|**0.8339**|**0.8241**|\n\nWe observe that extreme oversampling (5×+) causes significant performance degradation in both AdaTS and FOCAL due to embedding space collapse. To mitigate this issue, we added a cosine-based distance regularization to AdaTS following the VICReg framework (Bardes et al., ICLR 2022). The results demonstrate that FOCAL + AdaTS + DistReg significantly outperforms vanilla FOCAL + AdaTS, effectively preventing embedding collapse and maintaining robust performance even under extreme oversampling conditions.\n\n**4. Computational Complexity and Scalability** - [W4, Q2]\n\nWe appreciate the reviewer's concern about the practical complexity and scalability of AdaTS (W4). Regarding the tuning complexity, while AdaTS introduces additional hyperparameters ($\\lambda_{oc}$, $\\lambda_t$, and $\\tau_T$), our sensitivity analysis in Figure 4 demonstrates that the framework maintains robust performance across reasonable parameter ranges, reducing the burden of extensive hyperparameter search. AdaTS's design as a pluggable module means practitioners can initially use default parameter settings that work well across diverse datasets, with optional fine-tuning for domain-specific optimization.\n\nRegarding computational overhead (Q2), we present additional experimental results as we scale up the sequence length.\n\n**Sequence processing time (ms) with and without AdaTS**\n\n|Sequence Length|2|4|8|16|\n|--|--|--|--|--|\n|FOCAL + (AdaTS Overhead)|2.60 + (0.16)|5.13 + (0.31)|10.40 + (0.46)|20.37 + (0.96)|\n|TS2Vec + (AdaTS Overhead)|2.47 + (0.11)|5.07 + (0.21)|9.89 + (0.43)|20.83 + (0.76)|\n|TSTCC + (AdaTS Overhead)|2.60 + (0.09)|5.44 + (0.24)|11.44 + (0.49)|24.52 + (0.82)|\n\n**AdaTS Performance Across Different Sequence Lengths**\n\n|Dataset|Metric|2|4|8|16|\n|--|--|--|--|--|--|\n|ACIDS|Acc|0.9491|**0.9571**|0.9417|0.9296|\n||F1|0.8321|**0.8480**|0.7942|0.7785|\n|PAMAP2|Acc|0.8506|**0.8648**|0.8395|0.8328|\n||F1|0.8340|**0.8560**|0.8294|0.8224|\n\nWe find that AdaTS maintains consistent time overhead as sequence length increases, with optimal performance typically achieved at moderate lengths (4-8). Performance degradation at longer sequences (16) suggests a trade-off between temporal context and computational efficiency. We will include these analyses in the revised manuscript.\n\n**5. Multi-Modal Extensions** - [Q4]\n\nThank you for this insightful question about multi-modal temporal alignment. Since different modalities naturally exhibit different non-stationarity patterns, AdaTS currently processes each modality independently, computing modality-specific temporal assignments based on their intrinsic dynamics. Each modality's $\\kappa_n$ statistic is calculated independently, allowing adaptation to distinct temporal characteristics.\n\nOur experiments demonstrate that AdaTS integrates seamlessly with both unimodal and multi-modal frameworks as a pluggable module. We acknowledge that explicit cross-modal temporal alignment represents a promising future direction, and we will expand the manuscript to include detailed future work on temporal cross-modal coherence inspired by the reviewer's suggestions.\n\n**6. Hyperparameter Sensitivity** - [Q5]\n\nWe would like to kindly refer the reviewer to Section 3.2 and Figure 4, which present comprehensive sensitivity analysis for both $\\lambda_{oc}$ (ordinal consistency weight) and $\\lambda_t$ (dynamic temporal assignment weight) across ACIDS and PAMAP2 datasets. The analysis reveals that AdaTS maintains robust performance across reasonable parameter ranges, with optimal $\\lambda_{oc} \\in [0.6, 0.8]$ across both datasets, demonstrating that hyperparameter tuning burden is manageable."}]

Author rebuttal:

Meta-review: AdaTS is an adaptive “plug-in” for contrastive time-series pretraining. It adds (i) a time–frequency coherence similarity (TFC), (ii) ordinal consistency learning (OCL) to enforce relative similarity, and (iii) dynamic temporal assignment (DTA) that adjusts temporal weights per sequence. Integrated into TS2Vec, TS-TCC, and FOCAL, AdaTS improves accuracy across IoT, HAR, and UCR/UEA datasets, with ablations and sensitivity studies.

Reviewers agree the goal is important: handling non-stationarity and noisy similarities in SSL for time series. The framework is modular, easy to insert into existing methods, and DTA is a practical idea with clear motivation. Gains are consistent, especially under low-label settings, and reported overhead is small.

Reviewers noted that the conceptual novelty of TFC is questioned, as the related frequency-domain similarities (for example TF-C and MF-CLR) exist, and the paper does not yet make the unique advantages of TFC fully clear (both conceptually and empirically). OCL is close to standard triplet-style objectives (for example SoftCLT, TNC) and would benefit from stronger attribution (for example robustness under label noise, smoother embeddings).  Claims of “physics-guided” behavior need concrete case studies (for example gait cycles, vibration modes) or correlations to known phenomena. Several reviewers asked for clearer task definitions, more recent baselines from 2024–2025, embedding-space analyses (t-SNE, clustering metrics), and additional public datasets. One reviewer noted limits of STFT for highly non-stationary signals and suggested wavelet-based coherence. 

The authors added baselines and public results during rebuttal, improving clarity. Some reservations remain about the necessity of TFC versus prior frequency-domain metrics and about evaluation depth.

On balance, the method delivers reliable, cross-framework gains with small integration cost, and DTA is a useful contribution. Concerns around the novelty of TFC and evidence depth are real but appear addressable with targeted analyses rather than major redesign. With mixed-but-positive reviews (including one clear accept), I recommend acceptance.
Decision: Accept (poster)