AutoDA-Timeseries: Automated Data Augmentation for Time Series
Authors: Zijun Dou
Zhenhe Yao
Zhe Xie
Xidao Wen
Tong Xiao
Dan Pei
Venue: ICLR 2026 Poster (2026)
Keywords: time series analysis; automated data augmentation
Primary area: learning on time series and dynamical systems
TL;DR: We propose AutoDA-Timeseries, the first automated data augmentation framework tailored for time series, which adaptively learns augmentation strategies and consistently improves performance across diverse tasks.

Abstract: Data augmentation is a fundamental technique in deep learning, widely applied in both representation learning and automated data augmentation (AutoDA). In representation learning, augmentations are used to construct contrastive views for learning task-agnostic embeddings, while in AutoDA the augmentations are directly optimized to improve downstream task performance. However, existing paradigms face critical limitations: representation learning relies on a two-stage scheme with limited adaptability, and current AutoDA frameworks are largely designed for image data, rendering them ineffective for capturing time series–specific features. To address these issues, we introduce **AutoDA-Timeseries**, the first general-purpose automated data augmentation framework tailored for time series. AutoDA-Timeseries incorporates time series features into augmentation policy design and adaptively optimizes both augmentation probability and intensity in a single-stage, end-to-end manner. We conduct extensive experiments on five mainstream tasks, including classification, long-term forecasting, short-term forecasting, regression, and anomaly detection, showing that AutoDA-Timeseries consistently outperforms strong baselines across diverse models and datasets.

Published as a conference paper at ICLR 2026
AUTODA-TIMESERIBS: AUTOMATED DATA AUG- MENTATION FOR TIME SERIES
Zijun Dou', Zhenhe Yao', Zhe Xiel, Xidao Wen', Tong Xiao Dan Pei!
T Tsinguua University, Beijing, China
2 Alibaba Cloud Computing Company, Hangzhou, China
ttt douz (ouz)250maile t www.gnua..d....c..m
C GitHub:
Project:
ABSTRACT
Data augmentation is fundamental technique deep learning, widely applied in both representation learning and automated data augmentation (AutoDA). In representation learning, augmentations are used to construct contrastive views for learning task-agnostic embeddings. While in AutoDA, the augmentations are directly optimized to improve downstream task performance. However, both paradigms have key limitations: representation learning typically follows two- stage pipeline with limited adaptability, and current AutoDA frameworks are largely designed for image data, rendering them ineffective for capturing time se- ries-specific features. To address these issues, we propose AutoDA-Timeseries the first general-pun purposs AutoDA framework tailored for time series. AutoDA- Timeseries incorporates time series features into augmentation policy design and adaptively optimizes both augmentation probability and intensity in single-stage, end-to-end manner. We conduct extensive experiments on five mainstream tasks, including classification, long-term forecasting, short-term forecasting, regression. and anomaly detection, showing that MutoDA--Tiessee consistently outper- forms strong baselines across diverse models and datasets.
INTRODUCTION
Data augmentation refers to series of transformations that generate high-quality artificial data by manipulating existing samples, serving as a fundamental approach in deep learning to improve model performance and robusmness (Shorten & Khoshgoftaar, 2019; Wang et al., 2024). Existing ap- plications of data augmentation can be broadly categorized into two paradigms. The first paradigm representation learning, where augmentations are used construct contrastive samples, enabling models to learn task-agnostic representations (Chen et al., 2020; He et al., 2020). The second paradigm is automated data augmentation (AutoDA), which focuses automatically searching or generating augmentation strategies that directly optimize downstream model performance while reducing the reliance on manual design and tuning (Cubuk et al., 2019; 2020).
In time series analysis tasks, data augmentation is equally indispensable due to data insufficiency and homogencity (Wen al., 2020; Iwana & Uchida, 2021; Iglesias et al., 2023). As illustrated in Figure 1, these two application paradigms differ in their training pipelines when applied to time series analysis tasks. the representation learning paradigm, the encoder is first pretrained with contrastive learning on augmented views, and then transferred to downstream tasks through a sepa- rate fine-tuning stage, where the downstream model adapts to the learned representations (Yue al., 2022; Luo et al., 2023). However, a key limitation of representation learning lies in the adaptabil- ity of downstream models to the learned representations. For instance, recurrent neural networks (RNNs) are inherently designed for sequence-to-sequeeee celling at modeling long-term dependencies and dynamic evolution rather than capturing invariant
prediction (Sutskever et al., 2014), ex-
"Corresoonding author.

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representations emphasized by contrastive learning (Chen et al., 2020). In contrast, AutoDA follows a one-stage scheme where augmentations are jointly optimized with the downstream task. Aug- mentation policies, including the choice probability and intensity of transformation, tuned during training, producing high-quality and diverse samples tailored to the downstream task and directly enhancing downstream performance.
adaptively
Paradigm 1: Representation Learning
Stage I: Contrastive Learning
Representation Space
Stage 2: Downstream Tasks
Output
Contrastive
Model
1O lt2
Downstream
Raw Time Series
Loss
Paradigm Automated Data Augmentation
Augmentation Loss (Optional)
M
Apply
Augmeentation Policy
Output
Model
Downstream
Policy Generator
Label
Raw Time Series
Augmented Model
High Quality & Diverse Data
Downstream Tasks
Transformation
Downstream Tasks
Clasiisation
Lane Ten Furcaring
Bost Tea Fecealing
Repessioo
Anomaly Detection
Probabality Inteasity
Data Augmentation 2
Sciling
Timellep
Silee
Figure 1: Two application paradigms of time series data augmentation: representation learning and AutoDA.
While representation learning frameworks suffer from limitations in adapting to downstream mod- els. AutoDA provides promising alternative by jointly optimizing augmentations with downstream model training. However, existing AutoDA approaches have been predominantly developed for im- age data and are not directly applicable to time series due to the inherent differences between modal- ities. Even state-of-the-an (SOTA) of these AutoDA frameworks, including RandAugment (Cubuk et al., 2020), TrivialAugment (Müller & Hutter, 2021), UniformAugment (LingChen et al., 2020), and A2Aug (Li & Li, 2023), still face the following key challenges in the context automated time series augmentation:
Limited task generalization. Most existing AutoDA methods are validated on a single task. This narrow evaluation setting overlooks the fact that augmentation policies may not generalize well when applied to different time series tasks with distinct objectives.
Neglect of time series characteristics. Existing AutoDA frameworks ignore time series- specific features (e-g., autocorrelation, distribution, high-order features) when generating augmentation policies. Their assumption that transformations preserve semantic validity as in image domains fails for time series modality where critical time series features govern augmentation effectiveness, and modality-agnoste approaches risk distorting intrinsic data properties, yielding suboptimal strategies. For instance, carpuuncccwwappnggg... tations blindly applied without considering autocorrelation patterns may disrupt temporal dependencies, degrading downstream classification or forecasting model performance.
augmen-
Lack of adaptive policy learning. Previous SOTA AutoDA frameworks rely uniform sampling to determine both the types and intensities of augmentation transformations, treat- ing all transformations equally important without considering their varying impacts time series data. This uniform design fails to account for the fact that different transformations and intensities may contribute unevenly to the effectiveness of the augmentation policy, potentially leading to suboptimal or inappropriate augmentation policies.
To address these challenges, we propose general- -urrpose automated data augmentation framework for time series, LutoDA-Timesent employs an augmentation data generator that learns a com- bination distribution of selection probability and the intensity for each augmentation transformation,
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conditioned on the time series features. AutoDA-Timeserie offers several advantages: First, it pro- vides a unified one-stage framework that jointly optimizes augmentation policies with downstream task objectives, ensuring broad applicability across diverse time series tasks. Second, when choos- ing the optimal augmentation policy for each time series, it integrates multiple time series features, making it suitable for automated augmentation in the time series domain. Finally, the framework performs adaptive augmentation of both probability and intensity, which can more properly reflect the distribution of the optimal augmentation policy.
To summarize, our key contributions are as follows:
Comprehensive revisit data augmentation application paradigms: we analyze the limitations of existing paradigms, representation learning and automated data augmenta- tion, highlighting their restricted adaptability and the absence of time series-specific de- sign.
Tumeee-Timsssries framework: propose the first general-purpose automated data augmentation framework for time series, which incorporates time series-specific features into augmentation selection and jointly optimizes both augmentation model and down- stream model in a single-stage, end-to-end manner.
Extensive empirical validation: we conduct extensive experiments on five mainstream tasks, demonstrating the superiority, robustness, and generalization of AutoDA-Timeseriis through detailed evaluations and visualizations.
2
RELATED WORK
Time series augmentation refers collection of advanced techniques designed to artificially pand and diversity existing time series datasets. Previous studies have surveyed various time series augmentation transformations proposed for different downstream tasks, such classification and segmentation (Iwana & Uchida, 2021; Wen et al., 2020; Alomar et al., 2023; Iglesias et al., 2023; Mohammadi Foumani et al., 2024), or forecasting and anomaly detection (Wen et al., 2020; Iglesias et al., 2023; Semenoglou et al., 2023). Representative transformations include jittering (Salamon & Bello, 2017), rotation (Ohashi al., 2017), scaling (Ohashi et al., 2017), slicing (Pan et al., 2020), permuting (Um et al., 2017), time warping (Le Guennec et al., 2016), magnitude warping (Demir al., 2021), and several other techniques (Wen al., 2020). Beyond the level of individual trans- formations, recent research has further explored two broader paradigms for leveraging data augmen- tation: representation learning and automated data augmentation (AutoDA).
Representation learning aims to learn task-agnostic representations that can transfer across diverse downstream tasks. TS2Vec introduces hierarchical contrastive objectives together with contextual consistency (Yue et al., 2022). InfoTS leverages the information bottleneck principle and employs adaptive augmentations to generate diverse views, thereby learning more discriminative representa- tions (Luo et al., 2023). AutoTCL proposes contrastive learning framework with parametric aug- mentations (Zheng et al., 2024). AutoCL adaptively adjusts augmentation strength through cross- scale temporal consistency constraints (Jing al., 2024). CAAP learns adversarial augmentation policy that produces task-aware perturbations guided by contrastive objectives (Chang et al., 2024). FreRA leverages frequency-domain statistics adaptively decide augmentation direction and in- tensity (Tian et al., 2025). Despite their effectiveness, most representation learning frameworks adopt two-stage pipeline. In the first stage, multiple augmented views of the same time series are generated, and an encoder is trained using contrastive objectives to obtain task-agnostic representa- tions. In the second stage, the pretrained encoder transferred and adapted to downstream models. However, these two stages are decoupled: the augmentation strategy and representation learning in Stage are optimized entirely for the contrastive objective and cannot perceive feedback from the downstream model in Stage 2, particularly when the downstream model is not explicitly designed to leverage such representations. As a result, the learned representations may not always align well with the objectives architectures of downstream models, which limits the performance gains in practical scenarios.
AutoDA is proposed to generate optimal augmentation policies, mainly in computer vision (CV) domain (Yang et al., 2023). Early studies proposed two-stage -bsxe frameworks, such as TANDA (Ratner et al., 2017) and Automug (Cubuk et al., 2019), where smaller proxy model
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was trained to evaluate candidate policies. Although effective, these methods computationally expensive and often fail to generalize due to the mismatch between proxy and downstream mod- els (Cubuk et al., 2020). ReAugment uses variational masked autoencoder (VMAE) to reconstruct masked raw samples and learn their underlying data distribution, then applies reinforcement learn- ing adjust the VMAE's latent variable generate augmented sequences that preserve the original structure (Yuan et al., 2024). More recent work has shifted toward one-stage non-proxy AutoDA frameworks, which directly optimize augmentation policies with the downstream task. Represen- tative approaches include RandAugment (Cubuk et al., 2020), TrivialAugment (Müller & Hutter, 2021), UniformAugment (LingChen et al., 2020), and A2Aug (Li & Li, 2023). These methods eliminate proxy models and instead rely on the simple randomization or ensemble strategies to re- duce cost while improving downstream performance. However, applying such frameworks time series remains challenging, as they lack adaptive augmentation mechanisms and ignore modality- specific features that are crucial for preserving intrinsic patterns (Christ et al., 2018; Lubba et al., 2019).
3 METHODOLOGY
3.1 PROBLEM STATEMENT
Let D = (D,, Dm3 be a time series dataset, where D; (i = 1...m) is a univariate or multivariate time series. Let MM denote downstream model (e.g.. a classifier) whose trainable parameters are denoted as 8. We consider a set of time series augmentation transformations = (T. T2........ }, where Tj (j = 1.. n2) is an augmentation operator that can be applied to a given time series D; to produce an augmented view of D;- Our goal is to design an automated time series augmentation framework As parameterized by 0 that outputs a policy P: As (D:) for each D; E D. P. consists of two vectors: (i) a probability vector pi, where Ps,, E [0, 1] is the probability Tí is selected; and (īi) intensity vector t,, where t.j > 0 is the intensity of TỤ. After applying P; to D,, we can obtain the augmented time series P.) By performing this operation for the entire dataset D, we obtain the augmented dataset (D)) that is,
Ao(D) = {P.(D.) ED))
We then train the downstream model M on the augmented dataset by minimizing a task-related loss as follows:
0M arg min L (lMM.A((D))
AM
where L is the loss function of the specific task (e.g., mean squared error for forecasting, cross- entropy for classification, etc.). Finally, we evaluate the trained model M using the original dataset D, aiming to achieve superior performance with respect to the loss function L. The objective thus becomes finding the optimal parameter 8" for the augmentation framework Ag:
8* arg min (MM: D).
(3)
The automated time series augmentation is formulated as joint optimization over both the augmen- tation framework's 's parameters 0 and the downstream model's parameters 0y-
3.2 AUTODA- TIMESERIIS OVERVIEW
As shown in the Figure 2, time series feature-aware augmented data generator (denoted as Ag) is composed of multiple stacked Augmentation Layers (A)), each which is responsible for selecting
and applying one of the available transformations in the set T = (Ti, T,.
The k-th augmentation layer generates augmentation policy consisting of (i) series of probability (k) Pi.j indicating the likelihood of choosing transformation T, and (ii) a series of intensity (.) to apply chosen transformation. By stacking these augmentation layers, the framework can explore variety of transformation sequences, allowing for more diverse and potentially useful augmented data. The final output augmented time series is used to train single downstream model in single-stage, end-to-end manner, with a composite loss to update the parameters in the augmented data generator together with the downstream model.
(k)
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Training Downstream Model on Augmented (Synthetic) Data
Testing
Real Data
Augmeetted
Layer 1
Layer 2
Layer
Time Series
Update
Apply
Apply
Apply
Policy
Policy
T1
F'
F
Output
T2
Pie
Diversity
Composite
Tu
In
Loss
Update
Label
Output
Festure Victtor
Augmented Data Generator (Ag)
Transformation
Probabliny
Insensiiy
Adaptive Policy Generator
Downstream Model
Pist Augmentation Probability of Last Layer
Figure 2: Overall architecture of AutoDA-Timeserie.
3.3 TIME SERIES FEATURE EXTRACTION
Following the prior work (Qiu et al., 2024), we extracted 24 descriptive statisties each time series in the original dataset, forming a feature vector F; f=(D.), where fe(-) denotes our feature extrac- tion function. These features are effective across various time series classification and forecasting tasks (Lubba et al., 2019; Qiu et al., 2024). In our design, the feature vector F, remains unchanged and static across layers to preserve the global context the original time series, preventing distortion from sequential augmentations while stabilizing training.
3.4 STACKED AUGMENTATION LAYERS
Our framework Ag is composed of K sequential augmentation layers: A, = -
A o.. -
(K)
Each layer (/ receives (i) the input time series Dj* (k-1) from previous layer (raw time series D, for the first layer), (ii) the previous probability vector (k-1) (initialized as zeros), and (iii) the global feature vector F,. It then generates the probability ps
(k) and intensity (k) via MLPs ((k) and s(())
p f(k) (p. (R-1) ,.F).
(k) (k) (k-1) (D.).
(5) transformation Tra then sampled in each layer by a Gumbel-Softmax (Jang al., 2016) ap- proximation (denoted ggs), which ensures that the framework remains differentiable. The selected transformation Tr, is applied to D (*=1)) with intensity (A)) to generate the augmented time series:
Tr. go (T.P.
Pt (k)
D (k) Tr (D. (--1) (k)
(7)
By stacking these augmentation layers, the framework performs sequential transformations. The final output k(K)) (D,) is fed the downstream model. All layer parameters are jointly optimized with the downstream model via a composite loss backpropagation.
3.5 STRATEGIES FOR EXPLORATION AND EXPLOITATION
To balance exploration (experimenting with diverse transformations) and exploitation (converging on effective augmentations), we incorporate the following strategies:
3.5.1
LEARNABLE GUMBEI-SOFTMAX TEMPERATURE
We adopt a learnable temperature parameter the Gumbel-Softmal distribution to control the ran- domness of transformation sampling (Jang et al., 2016). Each augmentation layer maintains its
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temperature, and all temperatures optimized purely via backpropagation. A higher temperature encourages exploration by making the selection probabilities more uniform, while gradually low- ering the temperature increases determinism and helps the model converge to the most promising transformation choices.
3.5.2 COMPOSITE Loss FUNCTION
To maintain diversity in the transformation probability distribution, we encourage the augmentation layer to output diverse transformation probabilities. Therefore, in addition the task-specifie loss, we introduce diversity loss terms. To address the weight setting problem for multiple losses, inspired by previous work (Liebel & Körner, 2018), employ learnable weights in the final composite loss Lomppssite as follows:
Leomposine
Σ 1,1,2,3
+ 2w5
In(1 In(1 (
8
where: (1) L1 is the task-specific loss, e.g., mean squared error for forecasting, cross-entropy for classification. (2) L2 is an intra-layer diversity loss to encourage diverse transformations within a layer, which is defined as:
L
Σ E (1)
where K the number of augmentation layers, E.[:] means averaging over samples, p. E TR" is the augmentation probability vector for D, at the k-th layer, and Hp:))) denotes the Shannon entropy calculated as follows:
H(P 1
(K) +c), Σ Pij log(p!)
(10)
where n is the number of transformations, and is a small constant added for numerical stability (set to 10-10 in implementation). (3) La is an inter-layer diversity loss, which measures the divergence between the augmentation probability distribution of the current layer and that of the previous layer and is defined as:
K
L3
>
Es
(11)
where KL(P; (p,
(1)
Σj= 11-1) log Pi.j ()) - log (p:) [p])]
-og
(4) wis are learnable
weights achieve trade-off between diversity and task performance during the training.
This composite loss enables the augmented data generator and the downstream model to be jointly optimized in a fully end-to-end manner.
3.5.3 Raw TRANSFORM BIAS
To avoid overfitting to augmented data, we add bias term Pro that selects the raw data with proba-
bility Prst
Tr = Sags aga (T, (k))
with probability - (1-Prt),
[Ti
with probability Prb,
where Ti denotes the Raw (no transformation) operator.
EXPERIMENTS
We conduct extensive experiments to systematically evaluate the effectiveness of AutoDA- Timeseries five mainstream time series analysis tasks: classification, long-term forecasting, short-term forecasting, regression, and anomaly detection. Beyond quantitative comparisons with oate-offthheart baselines, also provide in-depth analyses and insights into AutoDA-Timeseria

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4.1 EXPERIMENT SETUP
Implementation Table 1 summarizes the benchmarks, evaluation metrics, and representative downstream models for each task. Following the prior work (Zheng et 2024), evaluate on rep- resentative downstream models and extend the scope by incorporating both classical and advanced architectures, covering convolutional, recurrent, Transformer-based, and generative paradigms, to assess the generalizability of AutoDA-Timeserie More detailed descriptions can be found in Ap- pendix
Table I: Summary of benchmarks, evaluation metrics, and representative downstream models.
Tasks
Benchmarks
Metries
Downstream Models
Classification
UEA (26 subsets)
Accuracy
TCN, ROCKET
Long-term: ETT (4 subsets), Exchange. Weather
MSE, MAE
Forecasting
RNN, Autoformer
Short-term: M4 (6 subsets)
SMAPE, MASE, OWA
Regression
UEA & UCR (6 subsets)
MSE, MAE
CNN, MLP
Anomaly Detection
MSL, SMAP, SMD
Precision, Recall, -coore
UNet, VAE
Baselines We compare AutoDA-Timeseriee with three groups of baselines ensure comprchensive and fair evaluation. We use NoAug as the control group, which does not apply any augmentation. tion learning, we adopt InfoTS (Luo et al., 2023), Au- toTCL (Zheng al., 2024), and TS2Vec (Yue al., 2022), which leverage data augmentation to construct contrastive views and learn task-agnostic representa- tions in two-stage manner. For automated data augmentation, we consider four state-of-the-art meth- ods: RandAugment (Cubuk et al., 2020), UniformAug- ment (LingChen al., 2020), TrivialAugment (Müller & Hutter, 2021), and A2Aug (Li & Li, 2023). More de- tailed descriptions of these baselines can be found in Ap- pendix B.
Detection (F1 scorc)
For representa-
Chosification
Regression (MSE)
(Accuracy):
Shoet thoet-tumm Borecsating (SMAFE)
Long-lurm Bervematnng (MS11)
(Dun))
UndloreAnggent (2020) (2024
424kg
Randfagreen (iean)
(3600)
TerinlAnggentt (3821)
132Vee (2003)
Figure Overall comparison of AutoDA-Timeseris with baselines across five time series tasks.
4.2 RESULTS
Figure 3 presents overall comparison of AutoDA-
Timeseries with state-of-the-art baselines across five time series tasks. observe that AutoDA- Timeseries consistently achieves the best performance, covering the largest area in the radar plot. Next, we provide a more detailed analysis for each task.
4.2.1 CLASSIFICATION
Setups Time series classification aims to assign a discrete label to each sample, which can be either univariate multivariate time series (Ismail Fawaz et al., 2019). We evaluate 26 subsets selected from the UEA archive (Bagnall et al., 2018), covering diverse domains such as audio recognition, human activity recognition, and healthcare monitoring. Following the prior work (Liu al., 2024), we use accuracy as the evaluation metric, and adopt TCN (Bai et al., 2018) and ROCKET (Dempster al., 2020) as representative downstream models.
Results As shown in Figure 4, AutoDA-Timesent achieves the best accuracy, reaching 0.730 (+6.7%) with TCN and 0.721 (+5.2%) with ROCKET, significantly surpassing the NoAug control. Traditional AutoDA methods (RandAugment, UniformAugment, and TrivialAugment) yield lim- ited or even negative gains, highlighting the gap in directly transferring image- augmentation policies to time series. Representation learning methods show instability: TS2Vec suffers severe

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TCN
ROCKET
3484
(5.00)
(V5.09)
CHAOPO
NOT
6.M
(12.7))
GOLLSN
(14.0))
Nokug
Indo7S
AwarTCL
Rand
Unilaet
Tririal.
A2/og
Ouis
Nodug
Iolo
AMorT!
TSSVee
Rund:
Untfar
Triidd
A2A0%
Our
Figure 4: Classification accuracy comparison of -eooD--Tieeeeies and baselines on TCN (orange) and ROCKET (blue). "*." " in the method names denotes *Aumment. Full results are provided in Table 15 and Table 16 in the Appendix.
degradation, while AutoTCL and InfoTS achieve only marginal gains. These results suggest that the augmentation policies of --moeseresssss can consistently boost classification accuracy and generalize across different downstream models.
4.2.2 LONG- AND SHORT-TERM FORECASTING
Setups Time series forecasting is fundamental task with wide applications in weather, traffic, energy, and finance. We evaluate AutoDA-Tinne Timeeserisss on both long- and short-term forecasting. For long-term forecasting, we use ETT (4 subsets) (Zhou et al., 2021), Exchange (Lai et al., 2018), and Weather (Wetterstation), with MSE and MAE as metrics, following the prior work (Wu et al., 2022). For short-term forecasting, adopt the M4 competition setup with six subsets (Spyros Makridakis, 2018), using SMAPE, MASE, and OWA as metrics. Representative downstream models include RNN-based forecasters and Autoformer (Wu et al., 2021).
Results As shown Tables and 3, AutoDA AutoDA-Timeseries achieves the best results on both long- and short-term forecasting. For long-term forecasting, AutoDA-Timesent attains the lowest MSE and MAE on both RNN and Autoformer. We also observe that representation learning suffers larger relative degradation RNN than on Autoformer, as Autoformer more compatible with learned representations. For short-term forecasting, SooDA-Timmseries again outperforms all baselines on RNN and Autoformer.
Table Comparison of long-term forecasting performance across baselines and AutoDA- Timeseries. "x." in the method names denotes *Augment. Full results are provided in Table 17 and Table in the Appendix.
Downstream Model
Methods
Metrics
NaAug
InfoTS
AutoTCL
TS2Vec
Rand.
Uniform.
Trivial.
A2Aug
Ours
MSE MAF
0.54D8
1,5163
1,4888
1,3851
0.5114
0.4416
0.5193
0.6342
0.3968
RNN
0.5381
1.5423
1,5166
1.4151
0.5117
0.4389
0.5148
0.6347
0.3930
MSF
2.4274
2.2761
2,2872
2.1240
2.4055
2,51116
2.3758
2.0155
1.9098
Autoformer
MAF
2,4883
2.3323
2.2626
2.1779
2.4655
2.5755
2.4254
2.0617
1.9548
4.2.3 REGRESSION
Setups Time series regression predicts continuous scalar from an input time series, differing from classification (discrete labels) and forecasting (future values) (Tan et al., 2021). In particular, generalizes forecasting by relaxing the requirement that the target must depend primarily on recent values, and has broad applications such as heart rate estimation from physiological signals (Reiss al., 2019) crop yield prediction from satellite observations (Yebra al., 2018). We evaluate six subsets from the UEA UCR archives (Tan et al., 2020), using MSE and MAE as metrics, with CNN and MI_ as downstream models.
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Table Comparison of short-term forecasting performance across baselines and AutoDA- Timeseries. "*." in the method names denotes *Augment.
Downstream Model
Methods
NoAug
InfoTS
AutoTCL
Vec
Rand.
Uniform.
Trivial.
A2Aug
Ours
SMAPE
11.84
124454
11.962
11.482
11,980
11,068
RNN
MASE
1.774
1.864
2.027
2.624
2.536
1.778
1.736
1.985
1.644
OWA
0.883
0.981
1.009
1.142
1.139
0.906
0.877
0.961
0.838
SMAPE
$7.854
47.219
38.875
39.389
63.573
69.034
59.541
39.456
39,425
Antoformer
MASE
14.865
15.216
10.406
7,790
48.076
16.301
15.729
7,818
7.762
OWA
6.020
3.359
4.154
3,482
14.915
6.807
6.308
3,499
3,490
Results Regression inherently relies precise continuous value mappings, making it highly sen- sitive to the quality of augmented data. As shown in Table 4, autoDA-Timeseries achieves state-of- the-art performance across diverse regression datasets, verifying the effectiveness of its task-adaptive augmentation strategy.
Table 4: Comparison of regression performance across baselines and the method names denotes *Augment. Full results provided in Table 19 and Table 20 in the Appendix.
"*." in
Downstream Model
Methods
Metrics
NoAug
AutoTCL
TS2Vcc
Rand.
Uniform.
Trivial.
A2Aug
Ours
MSE MAE
0.9285
L.1025
1.1290
1.0892
1.0951
1.4714
0.8875
1.6016
0.8921
CNN
0.6821
0.7386
0.7343
0,7211
0.7545
0.7477
0.6814
0.7160
0.6731
MSE MAE
1.2937
L.4036
1.4197
1.3441
1.2196
1,4032
1.2744
1.2157
1.0350
MLP
0.7010
0.7352
0.7348
0.7320
0.6695
0.7433
0.6729
0.6652
0.6420
4.2.4 ANOMALY DETECTION
Setups Time series anomaly detection aims to identify rare or abnormal patterns that deviate from normal temporal dynamics. The main challenge lies in the scarcity and diversity of anomaly sam- ples, making data augmentation particularly crucial. We follow standard benchmarks (Hundman et al., 2018; Su et al., 2019) and use Fi-score as the primary metric. Representative models include UNet (Gao et al., 2020) and VAE (Xu et al., 2018).
As shown in Table 5, anomaly detection is highly sensitive to augmentation, since inap- propriate transformations may erase or mimic rare anomalies, making them harder to detect. Nev- ertheless, -iieesrrrssseees consistently achieves superior results on both models, showing that adaptive policies enhance model robustness and generalize to augmentation--snnitive tasks.
Results
Table Comparison of anomaly detection performance across baselines and AutoDA AutoDA-Timeserie Timeseries. "x," in the method names denotes + *Augment, Full results are provided in Table 21 and Table 22 in the Appendix.
Downstream Model
Metrie
NoAug
InfoTS
AutoTCIL
TS2Vec
Rand.
Uniform.
Trivial.
A2Aug
Ours
UNet
F1
0.6991
0.6912
0.6944
0.6173
0.6844
0.7171
0.6886
0.6993
0.7478
VAE
F1
0.5592
0.4887
0-4871
0.4914
0.5610
0.4973
0.4945
0.5591
0.5761

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Layer
Leyer1
1.80
1.
Epech
E2007
Figure 5: Adaptive augmentation policy. Top: operator distribution over training epochs. Bottom:
entropy dynamics showing convergence in lower layers and diversity in higher layers.
4.3 MODEL ANALYSIS
Adaptive Augmentation Policy Visualization We investigate how augmentation policies evolve during training by visualizing augmentation operator probabilities and entropy across layers in the augmentation process (Figure 5).
The results reveal clear layer-wise differ- few
ences. Layer 0 rapidly converges operators (e.g., Raw augmentation), reflecting deterministic exploitation, while upper layers maintain higher entropy and more diverse poli- cies. This pattern illustrates the exploitation- exploration trade-off (Sutton et al., 1998), where lower layers stabilize the augmentation policies and upper layers remain adaptive, pro- viding complementary balance between sta- bility and diversity.
Raw Time Seres
Augmental Time Sertes
Feature-Space Consistency under Augmen- tation We examine whether augmentations preserve time series features shown in Fig- ure 6. The catch22 features of augmented data remain highly consistent with those of the raw data, indicating that TTmeessressss main- tains essential characteristics and further sup- ports our motivation of incorporating time series feature extraction (Section 3.3),
Figure 6: Feature-space consistency under aug- mentation.
CONCLUSION AND FUTURE WORK
In this paper, we proposed -eonD---meereesss. general-purpose framework that adaptively learns augmentation policies conditioned on time series features and jointly optimizes them with down- stream models. Experiments across diverse tasks verify its superiority and clear advantages over existing augmentation paradigms. In future work, we aim to extend the framework to real-world time series applications, which often involve diverse domains and complex dynamics.
6 ETHICS STATEMENT
This work focuses methodological advances in automated data augmentation for time series analysis and does not involve sensitive personal data. All experiments are conducted on publicly available benchmark datasets. We confirm compliance with the ICLR Code of Ethics.
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7 REPRODUCIBILITY STATEMENT
We ensure reproducibility through detailed descriptions of our method and experiments. The model architecture formally defined in the main text, and all implementation details are provided in the Appendix, including dataset descriptions, evaluation metrics, hyperparameters, training configura- tions, and experimental settings. All experiments are conducted under consistent protocols for fair comparison.
ACKNOWLEDGMENTS
This work is supported by the National Key Research and Development Program of China No..2024YFBBA 2024YFB4505900) and the China Postdoctoral Science Foundation (Grant No. No.2025M77)563) We thank Prof. Changhua Pei (Computer Network Information Center, Chinese Academy of Sci- ences) for helpful discussions and technical guidance.
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A IMPLEMENTATION DETAILS
All experiments were conducted on a workstation equipped with a single NVIDIA GeForce RTX 3080 Ti GPU. To evaluate the effectiveness of AutoDA-Timeseries we conduct experiments wide range of benchmark datasets across five mainstream tasks, including classification,, long-term forecasting, short-term forecasting, regression, and anomaly detection. The detailed statistics of the datasets are provided in Tables 6, 7, and 8.
We conducted experiments across multiple datasets with K {1,2,3, 4,5). and selected the hy- perparameter value that performs well on most datasets, namely K = 3. Therefore, in the main experiments, all datasets use 3.
B BASELINE DESCRIPTIONS
To comprehensively evaluate the performance of the AutoDA-Timeseta framework, the following baselines were applied to the same downstream models:
NoAug: No augmentation was applied; the downstream model was trained directly on the raw dataset.
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Table 6: Summary of benchmark datasets for time series classification.
Datasets
Code
Classes
Dims
Length
Test Size
Train Size
Type
Articulary WordRecognition
AWR
25
144
300
275
Motion
AtrialFibrillation
AF
3
640
15
15
ECG
BasicMotions
BM
100
40
40
HAR
Cricket
CR
1197
72
108
HAR
DuckDuckGeese
DDG
5
1345
270
50
50
Audio
BigenWorms
EW
17984
131
128
Motion
Epilepsy
EP
2066
138
137
HAR
FiRing
ER
65
270
30
HAR
EthanolConcentration
263
Spectro
FaceDetection
144
3524
5890
EEG
FingerMovements
100
316
EEG
HanuMovemennDirection
HMD
74
160
EEG
Handwriting
HW
152
850
150
HAR
Heartbeat
HB
405
205
204
Audio
Libras
LIB
180
180
HAR
LSST
LSST
2466
2459
Astronomy
MotorImagery
3000
100
278
EEG
NATOPS
NATOPS
180
180
HAR
PEMS-SF
PEMS-SP
144
173
267
Transportation
PenDigits
PD
3498
7494
Motion
PhonemeSpectra
PS
39
217
3353
3315
Audio
RacketSports
RS
30
152
HAR
SelfRegulationSCCP
SCPI
896
293
268
ERG
SelfRegulationSCP2
SCP2
2
1152
180
200
EEG
StandWalkJump
SWJ
2500
15
12
ECG
UWaveGestureLibbray
UWGL
315
320
120
HAR
Table 7: Summary of benchmark datasets for time series regression.
Datasets
Code
Dims
Length
Test Size
Train Size
Type
AppliancesEnnery
AE
24
144
42
96
Energy
FloodModelingl
FM1
266
202
471
Environment
FloodModeling2
FM2
1
266
167
389
Environment
FloodModeling3
FM3
266
184
429
Environment
LFMC
7
365
1510
3493
Environment
IEEEPPG
IEEEPPG
1000
1328
1768
Healthcare
TS2Vec (Yue et al., 2022): VS2 is a universal representation learning framework de- signed for time series, which enables representation learning across multiple semantic lev- els. It achieves this by hierarchically distinguishing positive and negative samples at both the instance and temporal dimensions, thereby capturing rich contextual information for diverse downstream tasks.
InfoTS (Luo et al., 2023); InfoTS is a contrastive learning-based method for time series augmentation. It generates augmented views of the input using parameterized trans- formations and learns representations by maximizing mutual information between them. InfoTS applies instance-level contrastive loss to retain fine- ine-grained semantic identity, par- ticularly useful for downstream classification tasks.
AutoTCL (Zheng et al., 2024): AutoTCL proposes a parametric framework for time series contrastive learning. It constructs two views using learnable augmentation module, and maximize their alignment via InfoNCE loss. The augmentation parameters are optimized with bi-level meta-learning strategy to enhance task performance.
Rundent (Cubuk et al., 2020): RandAugmenti is proxy-free automated augmentation framework that has achieved state-of-the-an (SOTA) performance image classification tasks, significantly optimizing performance compared proxy-based frameworks.
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Table 8: Summary of benchmark datasets for time series forecasting and anomaly detection. The "Dataset Size" column reports the number of samples in the training, validation, and testing splits, respectively.
Tasks
Datasets
Dims
Length
Dataset Size
Type (Prequency)
ETTmi. ETTm2
(96, 192, 336. 720)
(34465, 11521, 11521)
Electricity (15 mins)
ETTbI, ETTh2
7
96, 192, 336. 720)
(8545, 2881, 2881)
Electricity (15 mins)
Long-term Forecusting
Weather
21
196, 192, 336. 720)
06699,,571, 10540)
Weather (10 mins)
Exchunge
(96, 192, 336, 720)
(5120, 665, 1422)
Exchunge rate (Daily)
M4-Yearly
(23000, 0, 23000)
Demographic
M4-Quarteriy
(24000, 0, 24000)
Finance
M4-Monthly
(48000, 0, 48000)
Industry
Short-term Forecasting
M4-Weekly
(359, 359)
Macro
M4-Daily
14
(4227, 0, 4227)
Micro
M4-Hourly
(414, 0, 414)
Other
MSL.
(44653, 11664, 73729)
Spacecraft
Anomaly Detection
SMAP
25
100
(108146, 27037. 427617) (566724, 141681, 708420)
Spacecraft
SMD
38
100
Server Machine
Trivial Augment (Müller & Hutter, 2021): TrivialAugment is a tuning-free, proxy-free au- tomated augmentation framework that has demonstrated SOTA performance in image clas- sification tasks.
(LingChen et al., 2020): UniformAugment is proxy-free AutoDA framework achieving high efficiency and comparable performance in image classification tasks with theoretical supports.
UniformAugment
A2Aug (Li & Li, 2023): A2Aug is proxy-free AutoDA framework that trains multiple downstream models in parallel with different augmentation transforms and combines their outputs via ensemble learning, achieving SOTA performance in image classification tasks.
For CV-based AutoDA baseline, we did not naively apply these methods. Instead, we performed a rigorous timesseriss-ppeciee adaptation of each method. Specifically, we made three categories of modifications:
Replacing image operations with time-series transformations. The original RandAug- ment/ Peent/TrivillAugment families rely on image operations such as rotation, shear, and color jitter, which are not meaningful for time series. To ensure fairness, we replaced their aug- mentation set with standard time-series transformations such as jittering, scaling, time- warping, etc. This guarantees that all baselines and our method use the same valid aug- mentation set.
Preserving each method's original sampling logic. We strictly retained the core augmentatiion-seeccion mechanisms of each method: RandAugment preserves its N ran- dom operations + global magnitude M formulation. UniformAugment uniformly samples both operations and magnitudes. TriminlAugment samples one operation and magnitude per sample, following its original design. A2Aug learns augmentation weights jointly and ensembles operator logits adaptively.
Ensuring identical downstream settings for all baselines. For fair comparison, all base- lines use the same downstream models (RNN, Autoformer, etc.), the same data splits, se- quence lengths, and batch sizes
For time series-based representation learning baselines, we take the following measures to ensure fairness:
First, all representation learning methods (TS2Vec, InfoTS, AutoTCL) are implemented using their official open-source repositories. We strictly follow their default hyperparame- ter configurations, including the number of training epochs, batch size, optimizer settings, and the built-in augmentation pipeline. We do not modify any internal architectural com- ponents or training procedures. This ensures that the results are fully reproducible and not influenced by implementation choices our side.
Second, unlike image-based AutoDA baselines, time-series representation learning meth- ods already include augmentation operators specifically designed for sequential data. To
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ensure fäirness, we preserve the exact augmentation transformations defined in their offi- cial codebases. This prevents any methodological bias that might arise from altering or replacing their augmentation primitives.
Finally, to guarantee fairness in downstream evaluation, all baselines adopt the same down- stream configuration used in kept identical across all methods, and every method is evaluated under the same data splits. For representation learning baselines, we follow their standard protocol: the encoder first pretrained, and then frozen during downstream training while only the prediction head is optimized.
The downstream model architecture is
C ABLATION STUDIES
To verify the effectiveness of our key insights and the architecture designs introduced in Section 3, we conducted ablation studies. We remove each component from a complete and evaluate their impacts by performance degradation.
The results are presented in Figure 7 and Table As shown in Figure most points lie above the di- agonal, indicating that incorporating time series features, joint optimization, dynamic temperature, and composite loss consistently improves the performance of AutoDA-Timeseries on the classifi- cation task compared to their ablated versions. These results validate the necessity of the overall framework design, showing that each component contributes positively to the final performance, while removing any of them leads to performance degradation.
As shown in Table 9, first, disabling Time Series Features increased the MSE by up to 14.4%, which underscores the need for these features guide augmentation, verifying ing augmentation policy generation conditioned on time series features. Second, removing Joint Optimization of probabilities and intensities led increase in MSE of up to 7.6%, which empha- sizes the importance generating the optimal combination of transformation types and strengths. Finally, the xxploration-exploitatioo balancing strategies, including Dynamic Temperature and Com- posile Loss, all demonstrate clear effectiveness, reducing MSE by up to 7.4% and 8. % relative to their ablated counterparts. Overall, these findings emphasize that each component essential for the framework's performance and effectiveness in automated time series augmentation.
insight of perform-
To verify that namics, we conducted an ablation study based on the three major categories of Catch22 features as shown in Table 10. Specifically, different subsets of Catch22 capture key dynamic properties of time series:
meaningfully adapts augmentation strategies time series dy-
Linear and nonlinear autocorrelation features (e.g., CO flecee, CO.FirstMin ac) describe temporal dependency;
Distribution-shan shape features (e.g., DN Histogrammoode... 5, DN Wisttograaaoode.uue reflect whether the sequence contains sparse anomalies, skewness, or kurtosis;
Differential-base features (e.g., B.__BinaySStats.dff.... tions, short-term variation strength, and long-term stationarity.
capture local fluctua-
We removed each of these feature categories respectively and evaluated performance across 9 UEA datasets using ROCKET as the downstream model. The results show that removing any category consistently leads to noticeable performance drop. This indicates that semporal-structure features play an essential role guiding the learned augmentation policy: the model does not simply ap- ply general augmentation patterns, but instead relies on time-series-speciff dynamic attributes to adjust both augmentation selection probabilities and augmentation strengths, thereby achieving true adaptation to time series dynamics.
D HYPER-PARAMETE SENSITIVITY
-ime-se-issss involves two key papee-parameters:: the number of augmentation layers K and the raw transform bias Prb, which jointly determine the size of the augmentation search space and the proportion of raw samples retained during training. Specifically, the former controls how many
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Time Series Features
Optimiation
Dynamic Temperature
Compaske
0.-
0.2
62
Drath bn
02
Figure 7: Ablation study of AutoDA-Timeseries on TCN for classification.
Table 9: Ablation study of AutoDA-Timeseries on RNN for long-term forecasting.
Design Metrics
w/o Time Series Features MAF
w/o Joint Optimization
w/o Dynamic Temperature MAF
wlo Composite Loss
MSF
MSF
MAE
MSF
MSF
MAR
MSF
MAE
96
0.5071
0.4738
0:4884
0.4728
0.4903
0.4721
0.4906
0.4730
0.4849
0.4715
192
0.5697
0.4989
0,5592
0.5310
0.5540
0.5019
0.5529
0.5262
0.5536
0.5046
336
0.5876
0.5133
0.5468
0.4956
0,6049
0.5227
0.5843
0.5440
0.5552
0,4902
720
0,5804
0,5209
0.5648
0,5174
0.6232
0.5458
0,6026
0,5635
0,5777
0.5161
Avg
0.5612
0.5017
0.5398
0.5042
0,5681
0.5106
0.5576
0.5267
0.5429
0.4956
96
0.6013
0.5031
0.4749
0.4758
0.4635
0.4486
0,5090
0.4957
0.4714
0.4500
FT
192
0.7106
0.5381
0.5179
0.5072
0,5207
0.4987
0.5336
0.5110
0.5107
0.4630
336
0.7354
0.5542
0.5574
0.5246
0,5689
0.4900
0.5552
0.5210
0.5628
0.4881
0.7612
0.5499
0.6219
0.5579
0.5237
Avg
0,7021
0,5423
0.5396
0,5144
0,5434
0.4979
0.5549
0,5214
0,5380
0,4812
0.1236
0.2528
0.1188
0,2413
0.1299
0.2551
0.2328
Exchange
192
0.2238
0.3412
0,2607
0.3632
0.2201
0.3383
0.2135
0.3287
0.2049
0.3234
336
0.3745
0.4458
0.3945
0.4605
0.3610
0.4354
0.3989
0,4632
0.3582
0.4360
720
0.9984
0.7622
0.9839
0.7570
0,9810
0.7562
0,9866
0.7582
0.6920
0.6493
Avg
0.4301
0.4505
0.4395
0.4555
0.4235
0.4441
0.4322
0,4513
0.3409
0.4104
96
0.1842
0.2353
0.2066
0.2440
0,2066
0.2427
0.2053
0.2490
0.1736
0.2191
Wenthe
192
0,2286
0.2676
0.2483
0,2824
0.2407
0.2825
0,2474
0,2882
0.2263
0.2636
336
0,2948
0.3153
0.3287
0.3132
0.3236
0.3032
0.3209
0.2761
0.3050
720
0.3678
0.3617
0.4195
0.3992
0.3554
0.3517
0.3739
0.3692
0.3536
0.3534
Avg
0,2689
0.2950
0.31.36
0.2825
0.3068
0.2853
transformations are applied sequentially to each sample, where larger values increase data diversity but may also introduce excessive noise. The latter assigns probability to directly selecting the raw input, which acts as a regularizer to prevent overfitting to overly augmented samples.
As shown in Figure 8, both hyper-parameters have limited impact performance across different tasks. Specifically, increasing i yields stable results, with moderate values providing the best trade- off between diversity and reliability. For the raw transform bias, incorporating a small proportion of raw samples consistently stabilizes training and avoids degradation, highlighting the importance of balancing augmented and authentic data. Overall, these results indicate that -Timessrrise is robust to the choice of hyper- parameters.
In addition, our current implementation initializes the augmentation distribution using a uniform prior with an additional raw-transform bonus. To evaluate the sensitivity of our framework to this initialization, we conduct a controlled study on both classification (ROCKET-basee evaluated with accuracy) and long-term forecasting (RNN-basee evaluated with MSE and MAE) settings under four initial augmentation distributions:
Uniform distribution raw-transform bonus (the default setting in our paper);
Pure uniform distribution;
Random distribution sampled from Dirichlet(a 1). which centers around the uniform distribution with moderate variance;
Random distribution sampled from Dirichlet(a = 2), which produces samples closer to uniform but still with variability.
As shown in Tables 11 and 12, the final performance differences remain consistently small across all augmentation distributions. These results demonstrate that our framework is insensitive to the
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Table 10: Ablation study on Catch22 feature groups.
Dataset
Ours
Remove Autocorrelation
Remove Distribution
Remove Differencing
AWR
0.9800
0.9400
0,9467
0.9800
BM
1
1
CR
0,9861
0.9444
1
EP
0.9783
0,9275
0.9348
0,9203
ER
0.9741
0,9185
0.9296
0,9407
HB
0.7756
0.7610
0.7415
0.7512
RS
0.8947
0,8947
0.8421
0,8026
SCP1
0.8840
0,8464
0.8294
0.8328
UWGL 0,9313
0,9031
0.9125
0,9188
Average Accuracy
0.9353
0.9086
0.8979
0.9052
choice of the initial augmentation distribution, and that the learned augmentation policy remains stable and robust regardless of how the distribution is initialized. This indicates that the training dynamics of -amee--Timeseries are sufficiently strong to overcome any prior biases introduced at initialization.
Long-term Foreexeting (Input Ingot--Iopreddi--tt)
Be gegessioon
Anomaly Detectlon
1.0
32
mm
0.5
30
(a) Number of augmentation layers
Clasiifiation
(input 96 36predid--0
Ragression
Anomaly Detection
mm
0.3
04
307
391
05
(b) Raw transform bias
Figure 8: Performance of AutoDA-Timeseris under different ryper-parameter settings across rep-
resentative tasks.
Table 11: Sensitivity of AutoDA-Timesen to initial augmentation distributions on classification.
Dataset
Unifrom Raw Bonus (Ours)
Unitorm Dist.
Random Dist (c. 1)
Random Dist (a: 2)
AWR
0.9800
0.9500
0.9733
0.9633
BM
1
CR
0.9861
0.9861
EP
0.9783
0.9638
0,9420
0,9638
ER
0.9741
0.9222
0.9556
0.9296
HB
0.7756
0,7756
0.7415
0.7366
0,8947
0,8817
0.8882
0,9013
SCPI
0.8840
0.8703
0.8980
0,8805
UWGL
0.9313
0.9188
0.9270
0.9345
Average Accuracy
0.9353
0.9187
0.9235
0.9233
E GENERALIZATION ACROSS DATASETS
To further examine the generalizability of -inoD---Tmeeeiess. we conduct transfer experiments across datasets, summarized in Table 13. Specifically, train the downstream model together
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Table 12: Sensitivity of AutoDA-Timeserie initial augmentation distributions on long-tenn seng-termmmm fore- casting.
Dataset
Jeed_len
Metric
Unifrom Raw Bonus (Ours)
Uniform Dist.
Random Dist (ar
Random Dist (a
MSE
01,8849
0,5034
0,5098
0,5137
96
MAE
0.4715
0.4789
0.4795
0.4803
MSE
0.5387
192
BTTh1
MAE
0.5046
0.4912
0.4951
0.4956
MSE
0,5552
0.5658
0.5728
0.5762
336
MAE
0.4902
0,5027
0.5065
0,5069
MSE
0.5777
0.5536
0.5692
0.5734
720
MAE
0.5161
0,5003
0,5135
0,5139
MSE
0,3336
0.3590
0.3559
0.3559
96
0.3779
0.3918
0.3914
0.3914
MSE
0.4229
0.4250
0.4273
0.4273
192
ETTh2
MAE
0.4238
0.4277
0.4320
0.4321
MSE
0.4340
0.4453
0.4597
0.4593
336
MAE
0.4392
0.4503
0.4582
0.4582
MSB
0.4213
0.4472
0.4546
0.4498
720
MAE
0.4431
0.4590
0.4591
0.4505
96
MSE
0.4714
0.4787
0.4761
0.4763
MAE
0,4500
0.4581
0.4556
0.4539
MSE
0.5107
0,5077
0.5081
0.5280
ETTml
192
MAE
0.4630
0.4601
0.4735
0.4691
MSE
0.5628
0.5612
0.5615
0.6471
336
MAE
0.4881
0.4851
0.4897
0.5084
MSE
0.6071
0.6132
0.6023
0.6459
720
MAE
0.5237
0.5305
0.5188
0.5584
MSE
0,2019
0.2108
0.2156
0.2156
96
MAE
0,2850
0.2842
0.2896
0.2856
MSB
0.2601
0.2875
01,8877
0.2826
ETTm2
192
MAE
0.3225
0.3296
0.3265
0.3265
MSB
0.3485
336
MAE
0.3545
0.3727
0.3635
0.3639
MSE
0.4197
0.4582
0.,3228
0.4517
720
MAE
0.4165
0.4388
0.4358
0.4368
with augmentation policies on ETThl and directly evaluate the trained model ETTh2 and ETTm2, comparing with NoAug and UniformAugment baselines (the latter is included because it is the second-best method under the RNN downstream model, only inferior ours). As shown in the upper block (ETTh1 + ETTh2), all forecasting horizons, achieving the lowest average MSE and MAE, which demonstrates that the models trained with our framework generalize well to datasets with similar distribution. In the more challenging setting of ETTh1 - ETTm2, where the source and target distributions differ sub- stantially, the performance gap narrows, yet AutoDA-Timesenn superior to UniformAugmentt These results highlight that --oe----iesseisss not only enhances performance within single dataset but also exhibits strong potential for cross-dataset generaliza- tion, validating its robustness and applicability in real-world scenarios.
consistently outperforms the baselines across
remains competitive and clearly
F MODEL EFFICIENCY
To further evaluate the practicality of AutoDA-Timessries, we conduct efficiency experiments con- sidering three factors: parameter size, training time (ms/iter), and accuracy. As shown in Figure 9, AutoDA-Timeserin achieves a favorable balance between accuracy and efficiency. Compared with NoAug, SittDD--Timeerria brings consistent accuracy improvements with only moderate increases in parameter size and training time. Compared with more complex baselines such as AutoTCL, TS2Vec, and A2Aug, AutoDA-Timeseria delivers higher accuracy with significantly lower com- putational overhead. Although simple augmentation baselines such as UniformAugment exhibit shorter training times, they fail to match the performance of AutoDA-Timeseries, Overall, these
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Table 13: settings.
Generalization performance of AutoDA-Timesents on RNN under cross-dataset transfer
NoAug
UngformAugment MAE
AutoDA-Timeseries Timeseries
Settings
MSE
MAE
MSE
MSE
MAE
ETTh + ETTmm E77hh =ETh2
96
0.4761
0.4602
0.6486
0.5577
0.4431
0.4409
192
0.5418
0,4944
0.7172
0.5882
0,5146
0.4788
336
0.5566
0.5116
0.7366
0.6091
0,5374
0.4996
720
0.5416
0.5115
0.7727
0.6315
0.5354
0.5052
Avg
0.5290
0.4944
0.7188
0.5966
0.5076
0.4811
96
0.8668
0.6702
1.1370
0.7578
0.8663
0.6724
192
1.0198
0.7361
1.3273
0.8305
1.0087
0.7338
336
1.2997
0.8369
1.6327
0.9313
1.2791
0.8317
720
1.7294
0.9623
2.1243
1.0623
1.7105
0.9576
Avg
1.2289
0.8014
1.5553
0.8955
1.2162
0.7989
results highlight the advantage of trade-off, demonstrating its practicality for real-world time series applications.
in achieving an effective accuracy-efficieny
To complete our analysis, we next present formal analysis of AutoDA-Timessen complexity. During training, the computational complexity of the augmented model is O x B x x L). and the memory O(B the augmented model; only the downstream model is used, resulting in zero additional runtime or memory overhead. We provide a detailed derivation below.
computational
L). During inference, our framework does not invoke
The computational cost of MutoDA-Timeserii comes from two components: the policy generator (probability and strength generators) and the augmentation operators in the augmentation set T.
Time Complexity. The policy generator takes input the flattened feature vector in RCxd and the probability vector in R" from the previous layer. Both are length-independent vectors. Therefore, the cost for this part is O(B (Cd + n)), where B is the batch size, d is the channel dimension, C the number feature dimensions, and n is the size of the augmentation set. This cost does not depend on the sequence length L and is significantly smaller than the cost of applying the augmentation operators.
The dominant cost comes from the augmentation operators. Most transformations used in AutoDA- Timeseries (such as Jittering, Scaling, Time Warp, and Resample) involve pointwise operations or single interpolation along the temporal axis. Since the operations are performed over all time steps and across all d channels, their cost per layer is (dT.). Combining the two parts, the total time complexity of K stacked augmentation layers is O(K x B x L) + O(B x (Cd + n)) O(K x B x d x L), because the second term is much smaller than the first.
Memory Complexity. The memory cost consists three components.
The policy generator parameters. The MLP weights have size O + n)), which does depend on L. and is much smaller than the parameter size of downstream models such as Autoformer, VAE, or TCN;
The intermediate tensors during augmentation. At each layer the model stores the original time series and the augmented time series. The extra memory cost is O(B x d x L). Aug- mentation layers operate sequentially rather than in parallel. Therefore, the total additional memory cost remains O(B x d x L), the same as a single layer;
Downstream model memory, which is shared across all methods and does not affect the relative cost of AutoDA-Timeserts
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FingerMovements [28 dims]
Motorimagery (64 dims)
04755
0.E50
Uniferrs
aocurOS
0.58
0LE00
Ueffer
RoAae
EST
0.56
AGTOTTL
0.00
150
2000
5000
Training Time Imshterr
Training Time (ms/ttrr)
(a) Efficiency comparison on FingerMovements (b) Efficiency comparison on Motorlmagery dataset dataset (28 dimensions)
(64 dimensions)
Figure 9: Model efficiency comparison across datasets. The x-axis represents training time per iteration, the y-axis shows accuracy, and the bubble size reflects model parameter size.
G ROBUSTNESS UNDER LIMITED TRAINING SAMPLES
In practical scenarios, obtaining sufficient labeled training samples often challenging. data augmentation method that maintains strong performance under limited samples demonstrates better generalization under limited training data (Wen et al., 2020). To evaluate this property. we varied the training data ratio from 10%, 30%, 50%, 70%, to 100% of the original training set, and com- pared performance of NoAug with AutoDA AutoDA-Timeseries. We selected three representative tasks, including classification, long-term term forecasting, and anomaly detection. The model architectures and hyperparameters were kept fixed, and we reported Accuracy, MSE, and Fl-score for each task.
As shown in Figure 10, AmtoDA-Timeseric consistently outperforms NoAug under different frac- tions of training data. The advantage is particularly evident in low-data regimes, where augmentation substantially narrows the performance gap caused by limited supervision. Even when more data available, AutoDA-Timesett only alleviate data scarcity but also enhance robustness suggests that -aae-e-rrsse is not merely a remedy for data scarcity but a general mechanism enhance model generalization in diverse real-world scenarios.
remains competitive, indicating that learned augmentation strategies varying data scales. This finding
Classification (ROCKET-AWR)
Long-term Forecasting (RNN-ETTh1-nnput66 preeccctt66
Anomaly Detection (UNet-SMAP)
1.0
10
0.8
0.8
Accuracy
F1 Scare
0.6
0.6
04
64
50%
100%
10%
50%
100%
10%
50%
100%
Training
Training
Training
Figure 10: Performance comparison between NoAug and Timeseriis under varying train- ing data ratios (10%, 30%. 50%, 70%, 100%) across three representative tasks: classification (Ac- curacy), long-term forecasting (MSE), and anomaly detection (F1-score)
H WEIGHT DISTRIBUTION ANALYSIS
To further understand the effect of Timesserrsss on downstream model training, we ana- lyze the weight distributions of models trained with and without augmentation, they provide
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compact characterization of model stability and generalization. Figure 11 presents kernel density estimates of model parameters across five representative tasks, including classification (SelfRegu- lationSCP2), long-term forecasting (ETTh1), short-term forecasting (M4), regression (FloodMod- eling2), and anomaly detection (MSL). The distributions remain largely consistent in shape and centered around zero, indicating that AutoDA-Timeserie does not introduce abnormal parameter shifts or bias. Meanwhile, the five tasks exhibit distinct distributional patterns: ETThl and Flood- Modeling2 show narrow and almost sparse distributions, while M4 presents wider tails that reflect higher complexity. These results demonstrate that AutoDA-Timeserie Timeseries adapts effectively to diverse scenarios while preserving distributional stability.
Casiiiiction Setrititoos
Forecesting (EThh1
Short Forocast
Regressin (F)
Anomaly
-0.0
-025
8.00
0.75
0.50
-2.50
-0.25
2200
0.25
Figure 11: Comparison of weight distributions between models trained without augmentation (blue, NoAug) and with Timeseries preserves stable parameter distributions while adapting to lask-specific characteristics.
(red) across five tasks, demonstrating that AutoDA-
I PRIVACY ANALYSIS
To assess whether releasing augmented datasets could expose sensitive information from the orig- inal time series, we conducted statistical attack experiments compare privacy vulnerability by uniform (employed in RandAugment, UniformAugment, and TrivialAugment) or biased (employed augmentation selections. We try to reconstruct the original time series from an augmented time series dataset generated from set of augmentation transforms applied to the original seed time series, and evaluate the privacy vulnerability by the RMSE between the ground truth original time series and the reconstructed time series.
in
As detailed in Appendix I.1, a more deterministic reconstruction can be performed with known equal probabilities of augmentation, while in contrast, reconstruction with unknown probabilities of augmentation has to be modeled as a mixture-mode estimation.
As shown in Table 14, four groups of reconstruction are performed for comparison. To ensure fairness, G1, G3, and G4 utilize the same augmented time series dataset, and we control the G3 and G4 to iterate with the same time consumption. Due to the context limitation, more details can be found in Appendix 1.2.
The results are presented in Figure 12. First, G1 and G4, which simulate reconstructing from dataset generated by previous SOTA AutoDA frameworks, demonstrate a lower RMSE and time consumption than G2 and G3. This indicates the risk to data privacy when releasing augmented datasets with a fixed uniform augmentation policy. Second, the accuracy difference hetween G3 and G4 shows that the estimation of seed data can be easily misled when the augmentation probabilities are also jointly estimated for a mixture model estimation, proving the effectiveness for augmenting the time series without a fixed augmentation probability. Last, the RMSEs in G2 are higher than G3 and G4 with the same estimation model, indicating that the non-uniform augmentation probability in augmentation policy does increase the difficulty of reconstructing the seed data.
I.I RECONSTRUCT A SINGLE TIME SERIES FROM AUGMENTED TIME SERIES
This section discusses how to reconstruct the seed time series from augmented time series data based on a seed time series and a set of augmentation transformations T [Ti.j = 1,,,.... n.. when randomly sampling augmentation transforms and intensities.
Denote the original time series as c. Suppose the probability of selecting augmentation transform T; pi, the distribution of the augmented time series generated by T; is Y,, and the distribution of the
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203000
Table Reconstruction experiment group setup. AugProbs is whether the augmentation probabili- ties are equal, and ProbDist whether this prob- ability distribution is fixed and known the at-
tacker.
Group
Estimaation
PrubDist
AugProbs
Group (G1)
Deterministic
Fixed
Uniform
Group 2 (C2)
Mixture-model
Unfixed
Non-uniform
(a) Reconstruction RMSE
(b) Estimation time
Group (G3)
Mixture-model
Unfixed
Uniform
Group (GL)
Mixture-model
Fixed
Uniform
Figure 12: Reconstruction RMSE and time con-
sumption to reconstruct the original time series.
entire generated dataset is Xg. Then:
E(X,) Σ EP.E(Y())),
where ((Xg) is precisely the weighted mean expectation of all time series in the generated dataset, denoted meam (X,) = Mg. Next, the variance is given by:
Var(X,) = (Var(XX, p.)) Var(E(X, pi)),
where
(Varr(X, p.)) = Σ Ps Var Varaar(Y (Yi(c)
represents the weighted mean variance all subsets generated by different augmentation transforms.
In previous AutoDA frameworks (Cubuk et al., 2020; Müller & Hutter, 2021; LingChen et al., 2020), the transformation operators are predefined and fixed. Consequently, the distributions Y, can be easily derived apart from an unknown intensity range parameter t. Therefore, the distribution of X, is determined by the original seed data c, the intensity range parameter and the probability distribution (p:). These three can be viewed the prior for X3 and hence can be estimated with the observed samples of Xg. which correspond exactly to the time series the augmented dataset. result, if the augmentation transforms are selected with equal probabilities, t can be easily estimated, and the seed data c can be reconstructed accordingly, jeopardizing data privacy.
For illustration, consider toy example with specific seed time series and an augmentation transform set comprising three transformations:
Raw transform: (c)
Sealing transform: Y2(c) e 8, where the scaling factor follows a uniform distribution
~~ U - 2, 1]
Jittering transform: (c(c) = + n, where the noise n follows a Gaussian distribution n (0,,t²)
The expectation and variance of each subset are then:
E(Yi)
Var(Yi)
E(Y2) = -1
Var(Y2) =22202
E(Ys) = c,
Var(Ya) = 12.
Hence, if the augmentation transforms are chosen with equal probability = P2 = Ps = expectation and variance the entire augmented dataset are:
3 the
E(X,) c 2tc +
2t 2 C,
3
Var(X,) = (0 2 + +1/33 + (22 4122 + 21/3 2t
+2
1
812
8f + 3
3
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Since the average and variance of the augmented dataset can be computed easily, t can be estimated, and subsequently с can be inferred. By contrast, if the probability of augmentation selection is not equal, the model forms mixture model, making estimation significantly more complex.
Abstractly, when the selection probabilities are not necessarily equal, one must re-estimate the prior from observations of the distribution involving {pi). c, and t. However, when the selection probabilities are assumed to be equal, X, reduces to a distribution that contains only the unknown priors and which substantially reduces the difficulty of accurate prior estimation.
1.2 RECONSTRUCTIO EXPERIMENT SETTINGS
Given a predefined set of augmentation transformations, we apply these transformations to time series in the original dataset. Two types of datasets are generated with different strategies:
The generated time series data of all different transformations are directly mixed into the dataset D with equal probability.
The generated time series data of all transformations mixed into synthetic dataset D2 according to given probability vector.
From D1 and D2, , the original seed metrics as prior parameters estimated. For Dj, New- ton's method to estimate the intensity range parameters and the original seed metrics, denoted as Groupl. For D2, since the probability vector prior is unknown, it formulates mixture model esti- mation. Thus, the Expectation- pnteettation-Maiiizzaiiiii (EM) algorithm is applied to estimate the probability and the prior parameters of the corresponding distribution iteratively. We generated a dataset with unevenly sampled transformations and performed EM (denoted as Group2).
To ensure fairness in comparison, we also established a comparison group of applying EM on D1, learning the probability on its own (denoted as Group3), estimating with fixed probability (denoted as Group4).
In the experiment, specific formal modifications have been made to some augmentation transforma- tions to unify the problem form and accelerate the calculation. For example, the Raw transformation is replaced with Jitlering transformation with a minimal Gaussian noise. In addition, we have performed standard normalization on the original time series in advance to avoid the problem of inconsistent scales.
J FULL RESULTS
To provide a complete view of the experiment outcomes, we report the detailed results of all down- stream models across different tasks. Specifically, the classification results using TCN and ROCKET are presented in Tables 15 and 16, while the long-term forecasting results with RNN and Autoformer are summarized MLP in Tables 19 and 20. Finally, the anomaly detection results with UNet and VAE are provided Tables 21 and 22.
Tables 17 and 18. For regression, we present the detailed results of CNN and
K SHOWCASES
K.1
SHOWCASE FORECASTING CASES
To provide an intuitive understanding of how different augmentation strategies influence forecast- ing performance, we present case studies on ETThl dataset with a horizon of 96 steps, where the downstream model is RNN. As shown in Figure 13, the predictions from models trained with AutoDA- Timessriiss better capture the temporal dynamics and align more closely with the ground truth compared to those from other baselines.
K.2 SHOWCASE OF AUGMENTATION CASES
We visualize augmentation cases on the SCPI dataset to provide qualitative insights. Figure 14 and Figure 15 present two different samples, each showing the evolution of augmented time series across
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Table 15: Detailed classification results with TCN across baselines and AutoDA-Timesen "s," the method names denotes * *Augment.
InfoTS
AutoTCL
TS2Vec
Rand.
Uniform. (2020)
Trivial.
A2Aug (2023)
Datasets/ Methods
NoAug
Ours
(2023)
(2024)
(2022)
(2020)
(2021)
AWR AF
0.8933
0,9767
0,9800
0.9367
0,9133
0.9100
0.9433
0.9833
0.9533
0.3333
0,3333
0.4667
0.3333
0.4000
0.4667
0.3333
0.3333
0.4667
BM
0,5000
CR
0.9861
0.9861
0,9583
0,9583
0.9772
0.9028
0.9861
DDG
0.7200
0.5600
0.6000
0.2800
0.7400
0.7000
0.7000
0.6000
0.7200
EW
0.8168
0.8015
0.8168
0.7939
0.8321
0.6718
0.7634
0.8092
0.8702
HP
0.9783
0.9348
0.9420
0.9420
0.9783
0.9420
0.9783
0,9855
ER
0.7593
0.9185
0.8963
0.1667
0.8778
0.8815
0.8778
0.9037
0.9222
EC
0. 0,4030
0,2548
0.2890
0.3080
0.3004
0.2776
0.3118
0.3156
0.4068
FD
0,5000
0.6302
0.5499
0,5182
0.5000
0.5006
0.5000
0,5000
0,5000
FM
0,5900
0.6000
0.5500
0,5400
0.5700
0.5700
0.5300
0.5700
0.6200
HMD
0.4730
0.4730
0.4324
0.1758
0.4189
0.4054
0.4324
0.4595
0.4730
HW
0.5847
0,3647
0,4600
0.2753
0.5588
0.0812
0.6118
0.6671
0.4918
HB
0.7854
0.7610
0.7512
0.7317
0.7659
0.7512
0.7756
0.7756
0.7854
LIB
0.8222
0,8278
0.6667
0.7222
0,7667
0.1389
0.8222
0.9111
0,8500
LSST
0.3990
0,6310
0.5114
0.6196
0.4185
0.3491
0.6403
0.4124
MT
0.6100
0.5000
0.6000
0.6200
0.6100
0.6200
0.6400
0.6200
0.6700
NATOPS
0.8333
0.9389
0.8389
0.8944
0,8500
0.8444
0.8389
0.8334
0.8889
PEMS-SE SF
0.8324
0.7861
0.6821
0.5491
0.7977
0.4046
0.7514
0.8728
0.8497
PD
0.8645
0.9237
0.9423
0.9140
0.9525
0.8716
0,9580
0.8971
0.9634
PS
0.2320
0,2741
0.0954
0.1700
0.14
0.0790
0.1968
0.2103
1.1867
RS
0.9079
0.8882
0.8950
0.7566
0.9145
0,8882
0.9013
0,9211
0.9408
SCPI
0.8396
0.8703
0.8700
0.8567
0.8396
0.8601
0,8805
0.8669
0.8874
SCP2
0.5389
0.5667
0.5667
0.4611
0.5889
0.5667
0.5500
0.5611
0.6111
SWJ
0.3333
0.4667
0.4667
0,3333
0.4667
0.4000
0.4667
0.4000
0.7333
UWGL
0.7656
0.9094
0.8840
0,8156
0.8531
0.7063
0,8061
0,8219
0.7875
Average Accuracy
0.6847
0.6991
0.6812
0.5836
0.6939
0.6073
0.6915
0.7094
0.7304
three layers. The results indicate that the augmentation process preserves the global structure while introducing diverse variations, demonstrating the effectiveness of AutoDA-Timen Timescries in generating meaningful augmented data.
26

---

Published as a conference paper at ICLR 2026
Table 16: Detailed classification results with ROCKET across baselines and AutoDA-Timeseries. "*." " in the method names denotes *Augment.
InfoTS (2023)
AutoTCL
TS2Vec
Rand.
Uniform.
Trivial.
A2Aug (2023)
Datasets/ / Methods
NoAug
Ours
(2024)
(2022)
(2020)
(2020)
(2021)
AWR
0,9667
0,9767
0,9800
0,9833
0,9567
0,9433
0.9733
0,9900
0.9800
AF
0,4000
0.5333
0,5333
0,4000
0.4000
0.3333
0,4000
0.4000
0.4667
BM
0.7750
1
CR
0,9583
0.8889
0.8889
0.5972
0,9306
0.8334
0,9861
0.9861
1
DDG
0,6600
0,7000
0,7000
0,2600
0,6600
0,6200
0,5800
0,7000
0.7000
EW
0.6107
0.6565
0,6183
0,5954
0.6031
0,5649
0.6565
0.7252
0.7328
EP
0,9638
0,5725
0.9275
0.8551
0,9058
0.7971
0.9565
0.9420
0.9783
ER
0,9444
0 0,9593
0,9556
0.8
0,9296
0.9037
0.9222
0.9556
0.9741
EC
0,2928
0.4297
0,4373
0.4297
0.2928
0.2852
0,3042
0.2548
0,3156
FD
0,6200
0.6393
0.6348
0,5497
0.6379
0.6266
0.6263
0.6510
0.6328
FM
0,5900
0.6300
0.6200
0,6000
0,6300
0,5800
0.6100
0.6100
0.6500
HMD
0.5270
0.5135
0,5405
0,1351
0,5135
0,5000
0.5541
0,5000
0.5541
HW
0.3600
0.2200
0.2212
0,1600
0.3047
0.1141
0,3447
0.4800
0.3588
HB
0.7610
0.7415
0.7366
0.6341
0.7805
0.7512
0.7561
0.7659
0.7756
LIB
0.6889
0,8500
0.8556
0.7056
0,5833
0,3500
0,6389
0.8222
0.7167
LSST
0.6006
0.3978
0.5016
0.6156
0.5921
0.5393
0.6123
0.6415
0.5933
MI
0.5800
0.5900
0,5800
0,6100
0.5600
0.5500
0,5600
0,5500
0.6500
NATOPS
0.9167
0,9000
0,9056
0.8833
0.8889
0.8278
0.9167
0.9167
0,9167
PEMS-SF
0.5376
0.7919
0,7746
0.3584
0,3873
0.1792
0,4682
0.6301
0.5607
PD
0.9634
0.9663
9.966
0,9574
0.9520
0,9180
0.9634
0.9691
0.9711
PS
0,1837
0.10
0.1118
0.1288
1697
0.1184
0.1828
0.2120
0.1670
RS
0,8750
0.7434
0.8816
0.7895
0.8421
0.8289
0.8421
0.8684
0.8947
SCPI
0.8737
0.7406
0.7372
0,5290
0.8771
0.8532
0.8771
0.8567
0,8840
SCP2
0,5500
0.4778
0,4833
0,5278
0,5389
0,5389
0,5278
0,5556
0.6111
SWJ
0,5333
0.4667
0,4667
0,3333
0.7333
0.5333
0,5333
0.4000
0.7333
UWGL
0,8688
0,8406
0.8438
0.8875
0.8844
0.8594
0.8906
0.9156
0,9313
Average Accuracy
0.6856
0.6630
0.6887
0.5895
0.6752
0.6134
0.6801
0.7038
0.7211
Table 17: Detailed long-term forecasting results with RNN across baselines and AutoDA- Timeseries. in the method names denotes Augment.
Matosca
Notuz
Infor'
(2025)
AMSIICCL (2000)
IS (2022]
Rund. (2020)
Uniisen
(2033))
Insrall
(312/)
A2Asg (20253
Ours
Metric
MAE
MSE
MAE
MSE
MAN
MSE
MAEE
MSE
MAE
MSE
MAE
MAE
MSE
MAE
MSE
MAE
0.0034
0.5154
0.7253
0.508)
0.0454
0.0050
0,6141
0.5067
011587
11 1151141
0.0809
06123
0.5655
B6001
0.5614
11,8455
0,4715
100
0.6314
0.5794
0.5820
07661
[100)
0.7344
0.8138
0.6569
0,530
0.5053
115363
0.897
06615
0.5670
01067
B.6744
0.553
0.9315
336
0,5991
0,400
1,0161
03772
1.1044
0.2055
100L2
0,7556
0.173
0.5278
0.5642
0,5090
0.696
0.5K11
0,500
B5565
0.5533
0,2963
0.698
0,5550
1,1155
0.333
1.1164
0.223
1,1900
0.3308
0.520
0.5602
0.6595
0.3094
0.7020
0.592
67665
0.535
0.577
0.5161
106009
1.0033
1,0578
1.0033
1.0356
0.9404
1,0020
0.515
0.500
06733
06677
00253
0.550
0.5425
0.5633
96
0.4803
0.4266
1.3877
05731
1.5150
1,025
10807
0,333
0.4222
0.654
0
0.004
03235
D4127
05417
0.3336
0.379
0.0240
0.5151
13304
11200
27587
10032
0.981
11
0d211
0-106
1200
85587
0.4239
730
0.2499
0.9981
1.3281
1591
2500
1.2013
3.4651
1,5099
0.561
05864
0546
5.460
1515
1.4213
0,4431
Avx
0.0272
0.6995
2.2297
25000
22807
25339
20003
2.3742
0.560
0,5902
0.474
05538
0.5746
05502
1.067
0.4038
0
0.7656
0.512
0.554
0255
07621
0.635
0,515
0366
0.578
0461
09260
53555
03515
115107
0.6600
336
03231
0.57M
0151
0786
6.550
0,636
09131
0.003
0
6830
53551
0.577
0.5635
0.8151
720
030140
0.5099
B9346
05222
(1900
0.7534
03315
0
09630
08851
03189
0000
15500
01535
0.07)
0,5237
Ang
1020000
03350
D.545
03361
0:442
0.714
09151
07700
96
0.2646
033000
04450
05572
(1089
0.701
0,550
0.115
02383
0.330
021
0300
02355
03668
0150
0.0155
0
ETTmc
192
0.3312
03700
05002
(1957
0.055
12346
0,0000
03011
03559
1200
1959
02601
0
116
0-1197
0-1107
14188
100%
08114
1,0%%
80555
8501
02136
0,3345
0.5433
2.3000
0.411
0.600
04156
06174
0.3994
0.4429
1.2688
1.563
1.303
1.3014
1,9995
1.3145
0.3100
0.4236
0.3432
0.3014
03:18
03463
0.5699
0.2985
0,3311
Exchange
0.2766
161687
0.7809
17382
1,833
1.0094
1995
11871
1,1500
1,049%
0.154
0367
155
02611
01112
0-115
03693
04511
04315
0.1085
0.21KK
0.4981
12002
22760
0.4011
04401
1000
03352
0.340
730
10058
0,7766
27453
1.308]
2,000
1225
2,4336
1,3374
0.142
0.663
0.455
1.740
07764
05093
06633
0,035
0.6493
Avg
00-747
0.5787
[ 3.147
233707
2.560
3.135
3.3115
0.4103
1.595
0.45%
0.035
0.489
03635
04200
01657
015415
0
0:2561
Weathe
326
112
0.3021
0:33.6
031154
1.114
1:1156
016466
10009
1-225
1.072
05030
0.7269
111253
0.2302
0.118
0.6166
01585
0.3422
0.2/91
0.000
11241
0.2340
02414
0206
03396
03115
03081
01507
(1366)
013134
0.27%
0.2263
0.2636
0.3050
770
0-4254
0.3930
19997)
11119
1
10259
1861
0.998
0.10%
011624
0.055
3336
0.3765
844000
013516
0.311
003338
0.3597
1.5782
1.4276
0.9871
1.1125
0.2720
0,3058
0.2038
0.312
0.3100
0.356
0.3574
0.2553
Count
82
27

---

Published as a conference paper at ICLR 2026
Table 18: Detailed long-term forecasting results with Autoformer across baselines and AutoDA- Timeseries. " in the method names denotes *Augment.
Metbods
Nodug
TndoTS (2023)
AuteTCL
(2014))
TSINcz
(3122)
(2001)
Rand
Unifee
(2020)
Trivill
(2121)
AZAuz (2023)
Ours
Mario
MSF
MAF.
MSF
MAR
MSF.
MAF.
MSF
MAR
MSE
MAA
MS5
MA4
MSF
MAF.
MSF
MAR
MSB
MABI
96
Li063
0.7591
01641
0.7955
1,003
0.9193
0.3419
1,0060
0.7941
1,0410
0.5223
1.2311
0.8527
0.9523
0,8732
0.7455
192
0.9039
0.2761
0.9722
0.8126
0.9453
0.7887
0,8865
0.7404
1.8988
0.7904
1,0458
0.8234
1.1558
0.3145
0.5678
0.7885
0.305
0.754
196
100800
0.8071
0.5889
019776
6.9451
0.798
IL9277
0.74m
1.0834
087899
19590
0.6361
1 1799
0.882
0.0864
0.771
T8315
720
0.7799
0.9452
0.7815
0.9263
0.7064
1.0008
0.4376
1,0045
0.7850
1,0575
0.5270
1,290
0.8784
0.599
0.776
LA215
0.114
Ang
1,0531
1.398
0.9004
09963
96
3,5442
1.5563
3.3187
1.3570
3,0505
1,2503
3,0050
1.3190
3.2213
1.4055
3.2330
1.4385
25329
1,310
2.501
1 12940
2.4834
1.2322
192
2.7053
1.2536
3.1001
1.5 13430
3.1326
1.3836
4.592
1.3996
3,2790
1.4174
3.2193
1,4000
5.2522
1.3950
3.9979
1110
2.112
1.3442
335
[1996]
0.7951
3.00014
1.3340
1.006
1.3463
29778
117A
3,2731
3.2 3.2255
1.4515
3.236
1333
3.3667
15355
1716
1.3545
720
2.5034
1.31.0
3,0576
1,346
3,0566
1,3329
3,1055
1.3350
3,3630
1.4038
3/2516
1.4600
3,258
1.4151
3.2314
1.559
2.374
14445
Ang
2,5300
2,0756
1.0643
5.0036
1.000
1.090
5/666
5.1198
1,2503
5,2030
5.3154
3,2958
5.841
3.167
3,085
5 11900
3,679)
2.7655
96
1,8310
1.1192
1,0057
0.7893
1.1.00
0.5025
0.8148
0.7253
1,2186
0.8655
1.2275
0.5792
1,2325
0.800
1,0266
0.278
0.868
0,702
B3Tal
197
7794
110828
1.1134
08816
0.7915
(U8667
0.7621
1:2134
01
1.1197
0.771
03878
1004
T13T
336
1,0505
1,1021
0.7957
1,1319
01530
0.7615
0,9622
1.2157
0.5755
1.2237
0.6644
0.662
LO529
0514D
1,9893
1.0074
Ang
1,7361
1,7064
3,0045
1,0546
1.1179
1,131
0,8844
0.9075
1,3146
1,2135
1.3300
1,3175
1,2377
1.2261
10056
1107
1,0700
1
96
2.7817
1-3155
1:1061
1.351
3,0116
1.4213
3,8730
1.2085
2.8174
1.3056
3.5233
1
2.881
1,317
2.553
1,28K
1,3502
336
4.074
3.1818
1,3092
3.1447
1,3679
2,3167
1.1345
14425
3.3655
1.4706
3.3197
14534
3.364
1.511
15135
1.343
720
3.0671
1.430
3.1052
1,508
3.0466
1.3929
3.5414
1.4279
3.9905
1.4353
3.3176
1.4635
3.2859
1
3.1796
12304
1.49
1369
Asg
3,3829
3.5553
3,1530
3.1686
3,0570
3,1091
2.6247
2.7159
2,9015
3.0977
3.1889
3,3059
3,3917
33315
3.1188
3.1955
2.7044
2,7014
Exchange
193
2,0449
3.009
1.197M
4.7118
4.551
1.000
3.5771
4.7654
1,5085
1744%
2,0516
1.2911
1.4173
1550h
1730
3407
20675
2.5712
1.3145
1,373
14143
1.49M5
0.9194
336
1,8855
1.155
4.5605
1,7000
4.7636
1.7525
47336
1.7505
1,5007
1.1855
2,6509
1.3539
2,3447
L1812
1.600
L0358
1.5998
0.9914
130
2.7735
1:17
1.999
17619
43115
1.7607
48116
1 7761
7.9541
1 19652
1.0015
1.4613
2 2.5115
2.1155
12732
1.6965
1.0897
Ang
2.4436
2.3500
4.710
4.707
4,4717
4.750
4.74M)
3,3679
2,525
3.7300
2,5560
2
2.3577
2.1566
2,0144
1,5375
1.5653
96
5.6955
1.5336
0.4905
04111
0.677
0.801
(L550
0.4065
1.7655
1.5611
1/1100
1,5279
1.135
1.4190
1.6819
1.0191
1.3919
0.953
Weathes
192
3.1727
1,3155
0.113
115179
0.440
0.465
0.0157
0.4191
3.4955
1.5027
3.6112
1.5176
3,5850
1.5015
1.7155
1,0555
2.9921
1.3855
1.6
5.509
133274
06121
18025
0/172
03953
0:8856
0.541
3,5771
1.5330
3.4554
1.5033
5, 5,5172
1-4611
1.7180
11940
3,7300
1.3581
120
5.5349
1.338
018121
112940
0,438
0,6000
0.34m6
9.1654
(.862
3 1,9636
1.5146
29468
1.3117
18465
1.0787
3.0353
1.400
3.4855
0.5853
0.5336
0.4635
0,4947
3,4795
3.654)
3.2544
1,7460
1.7800
2.5225
2,9827
3" Count
6
0
0
Table 19: Detailed regression results with CNN across baselines and AutoDA "*." in the method names denotes * *Aumeent.
InfoTS (2023)
AutoTCL (2024)
TS2Vec (2022)
Rand. (2020)
Uniform.
Trivial. (2021)
A2Aug (2023)
Datasets
Metries
NoAug
(2020)
Ours
MSE MAE
0.6424
0.6463
0.6461
0.6457
0.6424
0.6424
0.6425
0.6458
0.6423
AE
0.6347
0.6465
0.6461
0.6458
0.6361
0.6349
0.6362
0.6458
0.6375
MSE 0.7370
0.7965
0.7414
0.7982
0.7370
0.7390
0.7387
4,8129
0.6602
FMI
MAE
0.6480
0.6473
0.6507
0.6591
0.6528
0.6555
0.6539
0.8405
0.6264
MSE
0.7813
0.5699
2.19
0.9154
0.5273
3. 3,1468
0.4536
0,4685
0,3875
FM2
MAE
0.3195
0.4305
0.4185
0.3684
0.2547
0.4643
0.2879
0,2652
0.2204
MSE
0,8647
1. 1.4001
1. 1.4228
1.3215
1.2162
1.5773
0.8622
1,1040
1.2221
FM3
MAE
0.6891
0.8537
0.8423
0.7890
0.8296
0.9066
0.7082
0.7535
0,8040
MSE 0.9789
0.9790
0.9786
0.9791
1.7238
0.9789
0.9789
0.9789
0.9768
LFMC
MAE
0.7544
0.7541
0.7494
0.7563
1.0769
0.7537
0.7544
0.7541
0.7484
MSF
156666
1.7569
1.7577
1,8753
1.7238
1.7439
1.6492
1.5992
1.4636
IEEEPPG
MAE
1.0466
1.0993
1,0990
1.1079
1.0769
1.0709
1,0480
1.0371
1.0018
Avg MSE
0.9285
1.0248
1.2899
1.0892
1.0951
1.4714
0,8875
1.6016
0.8921
Avg MAE
0.6821
0.7386
0.7343
0.7211
0.7545
0.7477
0.6814
0.7160
0.6731
1st Count
0
0
0
0
2
0
10
28

---

Published as a conference paper at ICLR 2026
Table 20: Detailed regression results with MLP across baselines and AutoDA-Timeseries "*." in the method names denotes * *Augment.
InfoTS (2023)
AutoTCL
TS2Vec
Rand.
Uniform.
Trivial. (2021)
A2Aug (2023)
Datasets
Metrics
NoAug
Ours
(2024)
(2022)
(2020)
(2020)
MSE MAE
0.6433
0.6425
0.6424
0.6435
0.6438
0.6425
0.6425
0.6438
0.6415
AE
0.6331
0.6318
0.6325
0.6298
0.6406
0.6335
0.6371
0.634
0.6354
MSE
0.2787
0.6555
0.6619
0.6332
0.3529
0.7047
0.3384
0.4155
0.2788
FM1
MAE
0.4062
0.5960
0.6148
0.6018
0.4774
0.6518
0.4455
0.4733
0,3885
MSE
3.1369
3.0971
3.0911
2.7806
2.3691
2,9632
2.9757
2.4156
1.7873
FM2
MAE
0.5184
0.5029
0,4051
0.4494
0.3179
0.4729
0.4364
0.3604
0.3223
MSE
0.8488
1.2171
1.2412
1.2197
1.0149
1.2935
0.8554
0.9558
0.7675
FM3
MAE
0.7416
0.8065
0.8744
0.8433
0.7046
0.8217
0.6666
0.6534
0.6530
0.9790
0.9789
0.9789
0.9789
0.9790
0.9789
0,9790
0,9789
0.9673
LFMC
MAE
0.7530
0.7546
0.7545
0.7546
0.7528
0.7544
0.7533
0.7543
0.7505
MSE
1.8752
1.8306
1.9025
1.8085
1.9581
1.8361
1.8551
1.8846
1.7675
LEEEPPG
MAE
1.1534
1.1195
1.1273
1.1131
1.1239
1.1256
1.0984
1.1157
1.1022
Avg MSE
1.2937
1,4036
1.4197
1.3441
1.2196
1,4032
1.2744
1.2157
1.0350
Avg MAE
0.7010
0.7352
0.7348
0.7320
0.6695
0.7433
0.6729
0.6652
0.6420
1" Count
0
0
0
0.
Table 21: Detailed anomaly detection results with UNet across baselines and AutoDA-Timeseries "*." " in the method names denotes Augment.
Datasets
MSI.
SMAP
SMD
Avg Fl
Metries
R
F1
R
F1
P
R
F1
NoAug
0.6215
0.9475
0.7506
0.7734
0.9692
0.8603
0.3290
0.9323
0.4864
0.6991
InfoTS
(2023)
0.6226
0.9475
0.7515
0.7734
0.9646
0.8585
0.3207
0.8371
0.4637
0.6912
AutoTCL
(2024)
0.6287
0.9458
0.7553
0.7677
0.9350
0.8431
0.3279
0.9297
0.4848
0.6944
TS2Vec
(2022)
0.618
0.9387
0.7453
0.6856
0.5722
0.6238
0.3268
0.9236
0.4828
0.6173
Rand.
(2020)
0.6283
0.9436
0.7544
0.7714
0.8971
0.8295
0.3176
0.4692
0.6844
Uniform.
(2020)
0.7144
0.9884
0.8293
0.7841
0.9392
0.8547
0.3249
0.8318
0.4673
0.7171
Trivial.
(2021)
0.6207
0.9448
0.7492
0.7679
0.9347
0.8431
0.3203
0.9080
0.4736
0.6886
AZAug
(2023)
0.6217
0.9475
0.7508
0.7743
0.9737
0.8626
0.3279
0.9279
0.4846
0.6993
Ours
0.7772
0.9906
0.8710
0.7888
0.9661
0.8685
0.3491
0.9045
0,5038
0.7478
Table 22: Detailed anomaly detection results with VAE across baselines and "*." " in the method names denotes *Augment.
Datascts
MSL
SMAP
SMD
Avg F1
Metrics
P
R
F1
P
R
F1
P
R
F1
NoAug
0.9015
0.4041
0.5581
0.9717
0.8652
0.9153
0.1507
0.3159
0.2041
0.5592
InfoTS
(2023)
0.9026
0.3962
0.5507
0.9948
0.5557
0.7131
0.1491
0.3137
0.2022
0.4887
AutoTCL
(2024)
0.9012
0,3894
0.5438
0.9948
0.5558
0.7132
0.1509
0.3158
0.2042
0.4871
TS2Vec
(2022)
0.9084
0.4017
0,5571
0.9948
0.5558
0.7132
0.1508
0.3154
0.2040
0,4914
Rand.
(2020)
0.9021
0.4151
0.5685
0.9863
0.8447
0.9100
0.1512
0.3163
0.2046
0.5610
Uniform.
(2020)
0.9043
0.4203
0.5739
0.9949
0.5559
0.7133
0.1512
0.3164
0.2046
0.4973
Trivial.
(2021)
0,9011
0.4125
0.5660
0.9948
0.5558
0.7132
0.1509
0.3160
0.2043
0.4945
A2Aug
(2023)
0.9001
0.4102
0.5635
0.9905
0.8393
0.9087
0.1517
0.3168
0.2052
0.5591
Ours
0.9032
0.4224
0.5756
0.9731
0.9225
0.9471
0.1521
0.3172
0.2056
0.5761
29

---

Published as a conference paper at ICLR 2026
322
Lio
342
100
112
(a) NoAug on ETTh1 (96)
(b) AutoTCL on ETThl (96)
InfoTS
UnformAugment
Paciton
(c) InfoTS on ETTh1 (96)
(d) UniformAugment ETTh1 (96)
300
110
340
100
122
(c) A2Aug on ETTh1 (96)
(f) AutoDA-Timeseries on ETTh1 (96)
Figure 13: Forecasting showcase ETTh1 dataset with horizon 96 using RNN the downstream model.
Layoro
Layor
MM mm fo M ma Auchun
MM MAW
Meigy N4 19
-30-
(a) Layer
(a) Layer
32.
ahe
do
(b) Layer
(b) Layer Leyord
Leyorz
20-
www.mm
mm mmmaa
(c) Layer
(c) Layer 2
Figure 14: SCP1 augmentation showcase
Figure 15: SCP1 augmentation showcase
across three layers.
across three layers.
30


Reviews:
[{"review_id": "Ns4lunNyli", "reviewer": "Reviewer_JEpj", "summary": "This paper presents AutoDA-Timeseries, a unified automated data augmentation framework specifically designed for time series tasks. Unlike traditional contrastive representation learning approaches, which adopt a two-stage pipeline, or existing AutoDA methods that are primarily image-oriented, AutoDA-Timeseries integrates both augmentation probability and intensity optimization into a single-stage, end-to-end framework. The method explicitly incorporates time-series–specific characteristics when designing augmentation policies and dynamically adapts them based on model feedback. Extensive experiments across five representative tasks show that AutoDA-Timeseries consistently outperforms strong baselines and demonstrates robustness across diverse datasets and architectures.", "questions": "See Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is comprehensive and well-organized, clearly articulating the motivation for domain-specific augmentation design.\n\n2. The end-to-end adaptive policy optimization framework is conceptually appealing and extends AutoDA beyond computer vision to time series effectively.\n\n3. Experiments cover a wide variety of tasks and datasets, showing consistent and convincing improvements over both random and rule-based baselines.", "weaknesses": "1. The literature review on both representation learning and AutoDA relies heavily on older references, more recent developments in time-series adaptive augmentation should be cited.\n\n2. In Related Work, the claim that “the learned representations may not always align well with downstream models” does not directly justify why current augmentation strategies are unsuitable. It requires stronger logical grounding.\n\n3. The proposed framework largely follows vision-based AutoAugment paradigms, but does not sufficiently account for time-series-specific characteristics such as temporal continuity, sequential ordering, and dynamic dependencies. Consequently, some learned augmentations may distort temporal patterns or reduce interpretability.\n\n4. The paper does not discuss sensitivity to the initial augmentation distribution, which is crucial to ensure stability and generalization across datasets.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "orUufXieAP", "reviewer": "Reviewer_CCnY", "summary": "The paper proposes AutoDA-Timeseries, a general-purpose automated data augmentation framework tailored specifically for time series data. Unlike existing approaches that either rely on contrastive pretraining or image-based AutoDA techniques, this framework incorporates time series–specific statistical features to guide augmentation policy design, and it jointly optimizes both the augmentation strategy and downstream task model in a single-stage, end-to-end manner. The method adaptively learns both the selection probability and intensity of each augmentation transformation through stacked augmentation layers, balancing exploration and exploitation during training. Extensive experiments across five major time series tasks—classification, long-term and short-term forecasting, regression, and anomaly detection—demonstrate that AutoDA-Timeseries consistently outperforms state-of-the-art baselines, highlighting its robustness and broad applicability.", "questions": "Please seek Weaknesses above.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The experimental results presented in the paper are comprehensive and compelling. The authors evaluate the proposed framework across a wide range of time series tasks—including classification, forecasting, regression, and anomaly detection—and consistently demonstrate improvements over strong baselines. The inclusion of diverse downstream models further highlights the generality and robustness of the method. Overall, the experimental evidence strongly supports the effectiveness and broad applicability of the proposed approach.", "weaknesses": "W1. \n\nOne key limitation of the paper is that it does not discuss or compare with policy-based augmentation approaches, particularly those that leverage reinforcement learning to adaptively optimize augmentation probability and intensity. This family of methods directly learns augmentation policies through reward signals tied to downstream performance and has been shown to be effective in automatically discovering task-specific augmentation strategies in several domains. Since the proposed framework also aims to learn adaptive augmentation behaviors, a discussion of how it conceptually relates to or differs from RL-based policy optimization would be valuable. Additionally, including comparisons with representative policy-based AutoDA baselines would strengthen the empirical claims and situate \nthe contribution more clearly within the broader landscape of automated augmentation research.\n\nW2. \n\nAnother weakness is the gap between the stated motivation and the technical realization. The paper argues that existing AutoDA frameworks overlook time series–specific characteristics such as temporal dependency and autocorrelation (Line 84 onwards), but it remains unclear how the proposed approach explicitly incorporates these properties into the augmentation optimization process. While the method uses descriptive statistical features to condition the policy generator, the paper does not clearly justify why these features are sufficient to capture the essential temporal structures that differentiate time series from other modalities. As a result, it is difficult to assess whether the framework meaningfully adapts augmentation strategies to time series dynamics, or whether it is primarily applying general augmentation patterns with limited modality-aware refinement. Further clarification or ablation focused on which time series characteristics drive policy decisions would strengthen this claim.\n\nW3. \n\nWhat is the meaning of $z=1,2,3$ in Eq (8)? Does it imply that $K=3$?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "afbFs4SrZn", "reviewer": "Reviewer_Mpcs", "summary": "This paper proposes AutoDA-Timeseries, a general data augmentation method for time series data. It incorporates time series features into data augmentation policy design and proposes an integrated optimization problem which relates the loss function in the downstream tasks and the augmentation probability and intensity. The proposed data augmentation algorithm is tested in different time series tasks, including time series classification, short-term/long-term forecasting, regression, and anomaly detection.", "questions": "Check the weakness part.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well written and easy to follow. \n2. It integrates reinforcement learning based data augmentation methods for different types of time series tasks, including time series classification, regression, forecasting and anomaly detection.\n3. Generally good review of data augmentation methods. \n4. Experiments in different time series tasks are performed to demonstrate the good performance of the proposed method.", "weaknesses": "1. The proposed framework is very similar to data augmentation methods in CV, but with some modifications for time series data. For time series data, the unique part seems [3.3 time series feature extraction], and the rest of the proposed method is very similar to existing methods in CV. The novelty of this paper is limited.\n2. Compared with other data augmentation methods for time series data, the efficiency of the proposed method is low. \n3. Some experiments are not convincing. The good part of experiments is that diverse time series tasks are tested. The weakness is that the selected downstream models are generally weak and not the SOTA methods. For example, for forecasting the RNN and Autoformer are selected, and the performance of both methods are not as good as SOTA. I suspect the proposed data augmentation method may increase the performance of those suboptimal algorithms. It may not be useful for SOTA methods. I highly recommend the authors test its performance of SOTA methods such as PatchTST.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Yz7y9szMWo", "reviewer": "Reviewer_juTF", "summary": "The paper introduces AutoDA-Timeseries, a novel framework addressing the specific needs of automated data augmentation for time series analysis, a domain where existing methods often fall short.   Key strengths include its general-purpose design applicable across five diverse tasks, the incorporation of time series features to guide policy generation, and extensive empirical validation showing consistent improvements over baselines. However, the theoretical justification for the controller objective is limited, and the link between learned policies and data characteristics is not well-analyzed. Moreover, some mathematical notations are ambiguous, and the computational cost of the search process is not discussed.", "questions": "- What is the computational complexity of one forward pass through k augmentation layers relative to sequence length L and dimension d? \n- How sensitive are results to the learnable loss weights wz; do they collapse to zero (diversity ignored) for some tasks?  \n- What specific time series augmentation transformations were included in the set $\\mathcal{T}$? How many transformations ($n$) were used?\n- How was the number of stacked augmentation layers $K$ determined? The hyperparameter study (Fig 8a) shows sensitivity analysis, but what was the value used in the main experiments, and how was it chosen (e.g., validation set)?\n- Was the learnable temperature for Gumbel-Softmax shared across layers, or did each layer have its own? Was a specific annealing schedule used, or was it learned purely via backpropagation?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Clear Problem Motivation: Section 1–2 convincingly describes the challenge of limited labeled time-series data and the difficulty of designing effective augmentations. The introduction connects to prior works, positioning AutoDA as a general extension for unsupervised or supervised forecasting.\n\n2. Comprehensive Experimental Evaluation: The framework's effectiveness is demonstrated across five distinct and mainstream time series tasks: classification, long-term forecasting, short-term forecasting, regression, and anomaly detection. This wide range of tasks supports the claim of a \"general-purpose\" framework.  Evaluation spans numerous benchmark datasets relevant to each task (e.g., 26 UEA subsets for classification, ETT/M4/etc. for forecasting). This provides robust evidence for the method's performance across different data domains and characteristics.   AutoDA-Timeseries is tested with diverse downstream model architectures for each task (e.g., TCN/ROCKET for classification, RNN/Autoformer for forecasting, CNN/MLP for regression, UNet/VAE for anomaly detection), demonstrating its compatibility and effectiveness across different modeling paradigms.   Comparisons are made against relevant baselines, including no augmentation, state-of-the-art representation learning methods (InfoTS, AutoTCL, TS2Vec), and recent AutoDA methods (RandAugment, UniformAugment, TrivialAugment, A2Aug), providing a strong context for evaluating performance gains.\n\n3. Scalable and Modular Design: The framework can plug into different backbone architectures, indicating extensibility. Such modularity makes AutoDA useful for practitioners exploring augmentation strategies under resource constraints.", "weaknesses": "1. Lack of Theoretical Justification for Objective: \n- The description of the composite loss (Sec 3.5.2) could be more detailed. While $L_2$ (entropy for intra-batch diversity) is standard, the motivation and formulation for $L_3$ (KL divergence for inter-batch diversity, Eq 10 [cite: 5386]) could be explained more clearly. The notation $p_{i}^{(current)}$ vs $p_{b,j}^{(prev)}$ also seems inconsistent regarding batch indices ($i$ vs $b$).\n- The notation for probability and intensity generation in Section 3.4  is slightly ambiguous. It states $p_{i,j}^{(k)}=f_{p}^{(k)}(p_{i,j}^{(k-1)},F_{i})$. Does this mean the probability for transform $j$ only depends on its own probability from the previous layer, $p_{i,j}^{(k-1)}$, or should the input be the entire previous probability vector, $p_{i}^{(k-1)}$? \n\n2. Insufficient Detail on Baseline Adaptation and Implementation:\n- The paper compares against several AutoDA methods originally developed for images (RandAugment, UniformAugment, TrivialAugment, A2Aug). Appendix B provides brief descriptions, but critically lacks detail on *how* these were adapted for time series. Without this, the comparison might not be entirely fair, as these methods might be suboptimal simply due to naive application.\n- The implementation details for the representation learning baselines (InfoTS, AutoTCL, TS2Vec) are also minimal. Clarity on this is needed for reproducibility and fair comparison.\n- How to optimize the framework is unclear. There are only two equations for the objective function. The final loss function is also missing. In equation 8, the definition of $L_z$  is missing.\n\n3. Ablation Study:\n- The paper omits a formal analysis of the computational complexity (both time and memory) of the proposed AutoDA-Timeseries framework. This makes it difficult to assess how the method's training and inference times scale with factors like time series length, dataset size, number of augmentation layers ($K$), or the size of the transformation pool ($n$). \n- The absence of a detailed parameter analysis (e.g., total parameters introduced by the augmentation generator $A_{\\theta}$ relative to the downstream model) also obscures the model's overall size and potential impact on training stability or overfitting.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)