PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
Authors: Jinju Park
Seokho Kang
Venue: ICLR 2026 Poster (2026)
Keywords: Time-Series Anomaly Detection
Representation Learning
Primary area: learning on time series and dynamical systems
TL;DR: PaAno is a lightweight yet effective method for time-series anomaly detection, leveraging patch-based representation learning with a simple 1D-CNN. It outperforms heavyweight methods based on transformers and foundation models.

Abstract: Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios. Moreover, they often fail to demonstrate significant performance gains over simpler methods under rigorous evaluation protocols. In this study, we propose Patch-based representation learning for time-series Anomaly detection (PaAno), a lightweight yet effective method for fast and efficient time-series anomaly detection. PaAno extracts short temporal patches from time-series training data and uses a 1D convolutional neural network to embed each patch into a vector representation. The model is trained using a combination of triplet loss and pretext loss to ensure the embeddings capture informative temporal patterns from input patches. During inference, the anomaly score at each time step is computed by comparing the embeddings of its surrounding patches to those of normal patches extracted from the training time-series. Evaluated on the TSB-AD benchmark, PaAno achieved state-of-the-art performance, significantly outperforming existing methods, including those based on heavy architectures, on both univariate and multivariate time-series anomaly detection across various range-wise and point-wise performance measures.

Published as a conference paper at ICLR 2026
PAANO: PATCH-BASED REPRESENTATION LEARNING FOR TIME-SERIES ANOMALY DETECTION
Jinju Park, Seokho Kang"
Department of Industrial Engineering, Sungkyunkwan University (apfhsk777, ..kag)) @skku.
ABSTRACT
Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as Transformers and Foun- dation models, they incur high computational costs and memory usage, mak- ing them impractical for real-time and resource-constrainet scenarios. More- over, they often fail to demonstrate significant performance gains over simpler methods under rigorous evaluation protocols. In this study, we propose Paich- based representation learning for time-series Anomaly detection (PaAno), a lightweight yet effective method for fast and efficient time-series anomaly de- tection. PaAno extracts short temporal patches from time-series training data and ID convolutional neural network to embed each patch into vector rep- resentation. The model is trained using a combination of triplet loss and pre- text loss ensure the embeddings capture informative temporal patterns from input patches. During inference, the anomaly score at each time step is com- puted by comparing the embeddings of its surrounding patches those of nor- mal patches extracted from the training time-series. Evaluated on the TSB-AD benchmark, PaAno achieved state-of-the-ar performance, significantly outper- forming existing methods, including those based on heavy architectures, univariate and multivariate time-series anomaly detection across various range- wise and posnt-wise performance measures. The source code is available at https: https://githus com/ jinnn ju/Panno
both
INTRODUCTION
Time-series data, a collection of temporally ordered observations, pervasive across wide range of domains, including industrial sensor measurements, financial market transactions, and healthcare monitoring (Yue 2022 Jia et al. 2024). A defining characteristic of time-series data the pres- ence of temporal dependencies among observations, shaped by the temporal context and underlying system dynamics (Lai et al.) 2018 Leung al., 2023)] Islam| (024).. However, these dependencies can be disrupted by various factors such as system faults, external disturbances, or human errors. Such anomalies may manifest as sudden spikes, abrupt drops, or sustained deviations certain ob- servations, as illustrated in Figure [T Time-Series Anomaly Detection aims to identify time points or segments within a sequence whose patterns deviate significantly from expected normal behav- ior (Paparrizos et al., 2022. Zhou et al. 2023a Sarfraz et al.) 2024). Because these anomalies often indicative underlying issues, accurate and timely detection is crucial for ensuring reliability and safety in real-world applications.
Recent studies on time-series anomaly detection have introduced large-scale neural network archi-
tectures, such Transformers (Xu al. 2021 Tuli et al.) 2022 Wu et al. 2022 Yue et al. 2024)
and Foundation models (Rasul et al. 2023 Zhou et al. 2023b Goswami et al. 2024). aiming to
capture long- long-term term temporal dependencies and cross-variable relationships effectively. Nevertheless, Sarfraz et al. (2024) and Liu & Paparrizos (202) have highlighted an illusion of progress in their methods by revealing limitations in existing evaluation practices, including structural flaws in bench- mark datasets and inadequate performance measures that rely on point adjustment or threshold tun- ing. When evaluated under rigorous protocols designed to mitigate these issues, these sophisticated
"Corresponding author.

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Published as a conference paper at ICLR 2026
large-scale neural network- srtdork-based methods did not demonstrate significant advantages over simpler methods (Sarfraz et al. 2024 Liu & Paparrizos 2024). Moreover, the high computational cost and memory usage further constrain their practicality in real-time resource-constrammed scenarios.
Global Paint
Contect
Shapelet
Time Time-Serter Value
RangeF
VUSROC
0.79
074
USR
Score
2480
3035
4000
1003
1102
Seavonal
Collective
Trend
0.09
Time Sertes Vanue
0.76
0.7
Point 0.41
AUCROC
5000
0.00
5500
3000
1940
Ground Truth Anomalyy
12303
12302
CATCH
Patoe(Cuur)
Maans'
TATT
RachTSTT
EAMAD
Figure 1: Illustrative results of PaAno, demonstrating strong Figure Anomaly detection per- capability in detecting diverse types of time-series anomalies. formance of PaAno and recent Datasets from TSB-AD-U (Liu Paparrizos 2024).
methods on TSB-AD-M.
In this work, we propose Patch-based representation learning for time-series Anomaly detection (PaAno), a lightweight time-series anomaly detection method based on patch-based representation learning. While recent studies on time-series anomaly detection have often relied on forecasting- based or reconstruction-basse plored and only limited subset of recent time-series representation learning studies has explicitly targeted anomaly detection (Zamanzadeh Darban et al. 2024). PaAno addresses this gap by in- troducing patch-level embedding space tailored to capturing subtle deviations time-series data while remaining invariant to small temporal shifts. Given time-series training dataset consist- ing only normal patterns, PaAno extracts short temporal segments, called Patches, by shifting a window one time step time. As the representation model, a compact 1D Convolutional Neu- ral Network (ID-CNN) is used to embed these patches into vector representations. The model is trained with learning objective that integrates metric learning and self-supervised pretext task, encouraging the embedding vectors to be informative and discriminative with respect to temporal of embedding
methods, representation-bate based methods remain relatively underex-
patterns in the input patches. After training, constructs memory bank vectors representing core patches from the training dataset. During inference, the anomaly score each time step computed based on the distances between the embeddings of the surrounding patches and their closest embeddings in the memory bank. Since PaAno does not rely on large-scale or heavily tuned architectures, it is fast and efficient, and well-suited for real-time anomaly detec- tion in resource-constraing environments. We evaluated the effectiveness of PaAno on the TSB-AD benchmark (Liu Paparrizos) 2024) with performance measures that avoid point adjustment and threshold tuning to ensure accurate evaluation. PaAno consistently outperformed existing methods across all measures.
Our main contributions are summarized as follows:
PaAno introduces a novel representation-bun framework that constructs a discriminative patch-level embedding space tailored for time-series anomaly detection.
PaAno uses lightweight ID-CNN model, enabling fast and efficient time-series anomaly detection compared recent methods that rely on heavy neural network architectures.
PaAno consistently achieves of-te-of-the-ar results on both univariate and multivariate time-series anomaly detection tasks across both range-wist wise and pisnt-wise performance measures.
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PaAno shows high robustness hyperparameter configurations, indicating that it does not require extensive hyperparameter tuning.
RELATED WORK
2.1 TIME-SERIES ANOMALY DETECTION
Time-series anomaly detection is formulated learning from time-series training dataset X (xi,-.., x consisting of sequential observations, where x, E TR represents the observation at time step t. predict whether query observation Xt, is anomalous. If each observation contains only single variable (i.e., d = 1), the task is referred to as univariate time-series anomaly detection. If there more than one variable (i.e., > 1), is called multivariate time- mene-series anomaly detection, where detection relies on the dependencies among multiple variables across time steps. Time-series anomaly detection typically categorized into three paradigms based on the availability in the training dataset (Choi et al.] 2021 Boniol et al. 2024 Zamanzadeh Darban et al. 2024): unsupervised, semi-supervised, and supervised. the unsupervised setting, the training dataset is unlabeled, and there is no explicit distinction between normal and anomalous observations. In the supervisd setting, only normal observations are present in the training dataset. In the supervised setting, the training dataset contains both normal and anomalous observations.
labels
In this study, we focus on semi-supervised time-series anomaly detection, which is widely regarded practical for real-world applications where labeled anomalies extremely scarce and costly to obtain. Time-series training dataset X = (xi...., xN) ) is assumed to consist solely of normal observations. The objective to learn model from the training dataset X to detect anomalies in query observations Xt,, At each time step t., the model produces an anomaly score E R. with higher score indicating greater likelihood that the corresponding observation xt, is anomalous.
2.2 TIME-SERIES ANOMALY DETECTION METHODS
A wide range of methods have been proposed for time-series anomaly detection, spanning from classical statistical techniques to modern deep learning architectures (Liu & Paparrizos (024)) We categorize existing methods into three groups based on the model architecture used: statistical and machine learning, neural network-basse based, and Transformer- based methods. Our proposed method PaAno belongs to the category of neural network-based methods. PaAno enables fast and efficient anomaly detection by utilizing a lightweight ID-CNN.
Statistical and Machine Learning Methods Methods in this category use statistical assump- tion or traditional machine learning algorithms to detect anomalies. Most of these methods were originally designed for point-wise anomaly detection in non-time-series data. They can be further categorized into four sub-groups. First, density- methods detect points segments that lie in low-density regions or deviate from estimated underlying distributions, including COPOD (Li et al. 2020), LOF (Breunig et al. 2000), KNN (Ramaswamy et al. 2000), and Matrix Profile (Yeh et al. 2016). Second, boundary-baat based methods find decision boundaries that separate normal and anoma- lous data, including OCSVM (Schölkopf al. 1999), IForest (Liu et al. 2008), and EIF (Hariri eet al., 2201)). Third, reconstruction-basee methods reconstruct the expected normal data and de- tect anomalies via residual errors. PCA (Aggarwal (017), RobustPCA (Paffenroth al. 2018 and SR (Ren et al.) 2019) employ dimensionality reduction. DLinear and NLinear (Zeng et al., 2023] use trend-remainder decomposition with a temporal linear layer for reconstruction. Fourth, clustering-based methods detect anomalies as deviations from learned cluster structures. including KMeansAD (Yairi et al. (001), CBLOF (He 2003), KShapeAD (Paparrizos & Gravano 2015 2017), Grrph (Boniol & Palpanas 2020), and SAND (Boniol et al. 20011)
Additionally, some of these methods have extensions designed for fixed-length segments and we denoted those with the prefix "(Sub)-" (e.g., (Sub)-PCA, (Sub)-KNN), following the practices in TSB-AD (Liu Paparrizos 2024).
Neural -etser Methods This category comprises conventional, non-Tramne non-Transformer neu- ral network architectures for time-series anomaly detection. Multi-Layer Perceptron (MLP)-based methods detect anomalies through reconstruction errors, including AutoEncoder (Sakurada & Yairi
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2014). USAD (Audibert et al., 2020), and Donut (Xu et al. 2018). Recurrent neural network (RNN)- based methods explicitly model sequences in time-series data to capture temporal dependencies, including LSTMAD (Malhotra et al. 2013) and OmniAnomaly (Su et al. 2019). CNN-based meth- ods utilize convolutional architectures to extract temporal features from time series, including Deep- AnT' (Munir et al., 2018) and TimesNet (Wu al., 2022). FITS (Xu al.] (2023) processes time
series by interpolation in the complex frequency domain. DADA (Shentu et al. 2025) learns robust representations through adaptive information bottlenecks and dual adversarial decoders, enabling zero-shot anomaly detection across mulates time-series modeling via Fourier enhanced Kolmogorov- Andold networks, achieving highly smooth normal pattern approximation with minimal parameters for efficient anomaly detection.
time-series. KAN-AD (Zhou et al. (2025) refor-
Transformer-Ban Based Methods Transformer architectures have been increasingly adopted to better capture long-range dependencies in time-series data. Anomaly Transformmm (Xu et al. [021 and
TranAD (Tuli et al. 2022) compute anomaly scores based on attention discrepancies. DCdetec-
tor Yang al.) 2023) employs dual attention with contrastive learning and CATCH (Wu et al.,
2025) applies frequency patching with a channel fusion module. PStchTST (Nie et al.) 2023) to- kenizes subseries-level patches with channel-independen weights. iTTransfommer (Liu et al.) 2024)
adopts an inverted view that swaps time and variable dimensions.
Foundation models, pretrained large-scale time-series data, adopted in time-series anomaly detection to enable zero-shot and few-shot detection capabilities. Chronos (Ansari et al.) and MOMENT (Goswami et al.), (2024) based on T5-style encoder-decoder architectures. Lag-
(024)
Llama (Rasul et al.J 2023). TimesFM (Das et al. 2024). and OFA (Zhou et al. 2023b) utilize decoder-only Transformer architectures.
2.3 ISSUES IN EVALUATION PRACTICES
Recent studies on time-series anomaly detection have often adopted evaluation protocols that in- troduce systematic biases, undermining the validity of reported results. First, several commonly used benchmark datasets suffer from structural flaws (Liu & Paparrizos] 2024). These include label- ing inconsistencies, where some anomaly-labeles observations are indistinguishable from normal patterns, and unrealistic assumptions about anomaly distributions, such as restricting anomalies to appear only once or at the end of a time series. Second, the conventional use of performance mea- sures that rely on point adjustment and threshold tuming can misleadingly inflate scores and hinder fair comparison across methods (Sarfraz et al. 2024 Bhattacharya et al. 2022
2024 Paparrizos et al.
Liu & Paparrizos 2024).
To address these issues, we adopt the TSB-AD benchmark (Liu & Paparrizos 2024), which mit- igates dataset-related flaws by correcting labeling inconsistencies and modeling anomalies under more realistic assumptions. In addition, we completely remove point adjustment from the evalu- ation protocol and include four threshold-indepeeddent measures, thereby eliminating biases from miscellaneous convention. Further discussions of evaluation measures are provided in Appendix C
PROPOSED METHOD
3.1 OVERVIEW
Local Temporal Dependencies Recent Transformer models have strengths in modeling long-term temporal dependencies by processing long time-series jointly (Zamanzadch Darban al. 2024 Liu et al., 2024; Wu et al., 2025). However, anomaly detection in time series often relies on localized patterns within short intervals. Time-series are typically strongly correlated with their immedi- ate neighbors but only weakly related to distant points, with this tendency particularly pronounced around anomalies (Xu et al. 2021 Yue et al. 2024). Using Transformers with global self-attention for modeling long sequences can dilute local temporal dependencies,
the mechanism is inherently Socality-agnostie leading to insensitivity to local context (Li et al., 2019 2023 Oliveira Ramos, 2024). Consequently, these models often struggle to capture the fine- grained local dynamics in shorter subsequences that are crucial for time-series anomaly detection, particularly for detecting point or contextual anomalies.
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fa
g0
Ratth Sampling
Preceding
Patch
Patch Patch
Posilive
Temporally
Similar
Projection Head
2
Lasa Computation Path
Farthes PortierPosstiveee
Leriplet
Neguative
Pr
Patch Encoder
Triplet Loss
Negative Patch
Series
h
Patch
Anchor Patch
Set Protest Tadk (Fabe)
preteet
consacutive
Are they
h
(h h
True Pocition
Pretext Loss
patches?
(Truo)
Sapueniallyy
Preceding
h,h
pr
CB
Preceding Patch
Figure 3: Training procedure of PaAno. The training dataset is split into patches. Using the patch set, three model components-a patch encoder, a projection head, and classification head-are trained with the training objective that consists of two losses. Triplet loss encourages temporally similar patches to have closer embeddings in the projected space, and pretext loss guides the patch encoder to learn temporal relationships by predicting whether two patches are consecutive.
Patch-Based Formulation To enable accurate and efficient anomaly detection, we introduce an inductive bias toward locality in PaAno. Our method PaAno motivated by recent advances in visual anomaly detection that leverage patch based representation learning (Defard et al. [202] Yi & Yoon 2020; Roth et al.] 2022; Yoon et al. 2023). These methods have shown superior performance on benchmark datasets such as MVTec-AD (Bergmann et al. 2019), where normal images within the same class exhibit highly consistent and repetitive spatial patterns, while anomalies disrupt these regularities.
We observe that time-series often exhibit analogous characteristics. Normal time-series display repetitive temporal patterns and strong local dependencies, whereas anomalies typically break such short-range regularities. PaAno aims to precisely capture these temporal dynamics through patch- based representation learning. Given a training sequence X. we extract overlapping fixed-length subsequences, referred to as patches, of window size w using a unit-stride sliding window, yield- ing P = (px ) where pt = (Xt,...,, X1) Patches serve as the fundamental units for anomaly detection. For each patch, apply instance normalization (Kim al. 2022b Yang al.) Wu et al., 2025), which standardizes all channels within the patch to zero mean and unit vari- ance. Reducing patch-level variability in mean and variance improves the stability of patch-level representations and increases robustness to distributional shifts such as regime changes or drift. To enrich patch embeddings, PaAno leverages the sequential continuity and temporal similarity across patches, which enable effective learning of local temporal dependencies essential for anomaly de- tection.
2023
Figure 3 provides schematic overview of PaAno, including its model architecture and training objective. The pseudocode of the training procedure presented Appendix A
3.2 MODEL ARCHITECTURE
The model architecture of PaAno comprises three main components: a patch encoder fe. a projection head and a classification head cp. The patch encoder fo is a ID-CNN that embeds temporal patterns from the input patch p E Tx into vector representation h R.. This patch-level embedding h serves as the input to both the projection head go and classification head The projection head go is an MLP that transforms into its projection z. This projected embedding is used for metric learning, encouraging the encoder fa extract features that are discriminative with respect to the temporal patterns in the input patch. The classification head eo is another MLP that takes the embeddings of two patches inputs to predict whether they temporally consecutive, thereby encouraging the encoder fo to capture sequential relationships among patches. Once the model is trained, only the patch encoder fo is retained for anomaly detection in future observations.
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3.3 TRAINING OBJECTIVE
Given a minibatch B - (p: ) sampled from the patch set P at each training iteration, the training objective for patch-based representation learning combines and a pretext loss ppeecex. The overall training objective is given by:
loss functions: triplet loss Luiplet
Lripler X Epeetest
(1)
where the weight À controls the contribution of the pretext loss ppeetext Details of each loss function are described below.
Triplet Loss The triplet loss is loss function introduced in deep metric learning to learn an embedding space where an anchor example is closer to positive example than to a negative one by specified margin (Schroff et al., 2013). To promote the patch encoder fo to extract embeddings that capture temporal patterns in the input patches, we adopt triplet loss so that patches with similar temporal patterns embedded close together, while those with dissimilar patterns are pushed apart.
For each patch p: E B as the anchor, the positive patch p.a is obtained by randomly shifting the anchor P: within time steps, excluding zero shift. This ensures that the anchor and positive patches exhibit similar temporal patterns. We define the negative patch as the farthest negative, chosen as the patch in the minibatch that has the largest cosine distance to p; in the embedding space, (i.e., Pj E B \ {p:} that maximizes dist(fe (P,))) After the patches Pi. P and P; are encoded and projected into Zi, z and z through the encoder fo and projection head go, the loss Cuiplet is computed as:
M Σ
Criplet
max (0, (0,distzz, 2,,, - dist (2,,,,,, + +0)
(2)
M
where 5 the margin hyperparameter that denotes the minimum distance the anchor must be closer to the positive than to the negative and M is size of minibatch. Minimizing Luiptet encourages the anchor to be closer to the positive patch than to the negative patch in the embedding space, thereby making the embedding space robust to small temporal shifts while remaining sensitive to meaningful differences. We expect this to produce well-organized clusters of normal patches, with unseen future anomalous patches lying far from them and thus being effectively identified.
The pretext loss inspired by Yi & Yoon (2020) study on visual anomaly detection based patch-based representation learning, where the training objective includes patch-level classification task to predict the relative positions of image patches. This pretext task enhances patch embeddings by promoting spatial awareness. We adapt this idea time-series data by formulating patch-level classification task that predicts whether two patches are temporally consecutive, thereby guiding the patch encoder fe better capture temporal relationships among patches.
Pretext Loss
For each anchor patch p. E B, we select the preceding patch p, that is exactly w time steps ahead, such that it temporally preceding to the anchor P;. We also draw U random patches, p. ..... pl from B \ {p:}. The classification head cy takes pair of patch embeddings as input and outputs the probability estimate indicating whether the two patches are temporally consecutive.
After obtaining the embeddings for p.. p. and pij. using the encoder fo, the loss Epreees is computed as:
M Σ 1=1
U
Epeceext M
1
log co (b,,
1
Σ 1=1
log(1 co (h,, (agh))
(3)
U
Minimizing Epretest encourages the classification head to assign high probability to pair con- sisting of an anchor patch and its preceding patch, and low probability to a pair consisting of an anchor patch and random patch. This loss is applied only during the carly stage of training to expedite the learning of temporal relationships among patches, thereby stabilizing representation learning when the embedding space is not yet structured.
3.4 MEMORY BANK
After training, the patch encoder fo forms a embedding space where similar normal patches are tightly grouped, while distinct normal patterns occupy different regions of the space. A memory

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bank M is constructed as the set of embeddings of patches Pa P obtained using fo:
M (ff(p:) Pr E P).
(4)
For anomaly detection, it serves collection of cohesive clusters of normal patches present in the training dataset X. providing clear reference for identifying anomalies in future observations.
A practical consideration is that the memory bank M grows with the size of the training dataset X. llading to increased computational costs and storage requirements for anomaly detection (Yi & Yoon 2020; Roth et al. 2022). To address this, apply coresct subsampling reduce the size of M. We perform K --eans clustering on M to derive K clusters. For each i-th cluster, we select the vector m, E M that is closest to its centroid. The resulting K representative vectors, denoted by m MA, , used to construct reduced memory bank Mr
= (m,)
(5)
This reduction preserves representative coverage of the original memory bank while significantly reducing its size, thereby enhancing the efficiency of anomaly detection.
3.5 ANOMALY DETECTION
During inference, the patch coder fo and reduced memory bank M are used to compute the anomaly score St. for query time step t..
Unseen Time-Series Data
Reduced
Query
Patch Eneoder
Memooy Rank
Patch
Trained
Patch Fommolation
Retrieve Top-k
Time Series points
Nearest Normal
Anomaly Score
fe
Embeddings
We first compute patch-level anomaly scores for the patches that include the query time step
Average of
h
133
patch containing time Pc-
m
f.. Let Pt, (pt) {p:) denote the set of these patches, where each patch P. = (x.,..., x1+ is collection of the most recent observations starting at time step t. Each patch p. Pt. is embedded using the encoder So as fo(p.). This embedding then compared to the vectors in the memory bank M for anomaly scoring. Specifically, the
5p-
Campute Average Distance to Retrieved Neighborr
sig
5gp)
Time
Win Se wfth
Aggregate Patch Scores
Get Pateh-wise
Anumaly Score
Anomaly Score
Figure 4: Anomaly detection procedure of PaAno.
nearest neighbors in terms of cosine distance are retrieved from M, denoted by m, ..... (k)
(1)
The patch-level anomaly score for p.. denoted S(p:), computed as:
S(p:)
dist(ffo (p:)), m.))),,.)) (2)).
This score reflects how dissimilar the patch is from the normal patterns learned from the training dataset X. For an anomalous patch, embedding fails align with any clusters of normal patches in the memory bank, resulting in a high anomaly score.
The final anomaly score for the query time step t., denoted st,, obtained by averaging the patch- level scores of all patches in P..:
1
S1.
Σ
S(p.).
P
P.EPL.
The anomaly score st. reflects the temporal context across the surrounding patches. higher value of st, means that the behavior around the time step t. is substantially different from those observed in the training dataset X, and thus indicative of a potential anomaly. Figure 4 illustrates the anomaly detection procedure of PaAno, and its pseudocode is fully provided in Appendix

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4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
TSB-AD Benchmark We used the datasets provided by the TSB-AD
Table 1: Statistics of the TSB-AD benchmark.
benchmark (Liu & Paparrizos 2024). which were specifically curated to ad- dress critical limitations existing evaluation practices for time-series anomaly detection. The summary statistics are presented in Table [
Category
Split
# Time-Series
Avg. Length
Anomaly Ratio
Tuning Eval
48
47143.3
3.5%
TSB-AD-U
530
51866.7
4,5%
Tuning
20
98164.1
5.7%:
TSB-AD-M
Eval
180
108826.7
5.0%
Figure [ shows examples of the time-series datasets. The datasets are categorized into two groups based on the number of variables: TSB-AD-U for univariate and TSB-AD-M for multivariate time series. Each group is further divided into a "Tuning" set for hyperparameter optimization and an "Eval" set for performance evaluation. Additionally, each time series has a predefined split point, where the preceding segment is designated for the training dataset.
Implementation Details The patch encoder fa was a ID-CNN consisting of four 1D convolu- tional layers followed by global average pooling layer with output size 64. The projection head ge was a two-layer MLP with dimensionality of 256 and the classification head oo was a single-layer MLP. The model was trained for 200 iterations using the AdamW optimizer with a minibatch size M of 512 and a weight decay of le-4, The pretext loss weight À was linear decayed from to during the first 20 iterations and fixed O thereafter. The memory bank size was set to 10% of the original patch set P. The number of nearest neighbors k in the anomaly scoring function was set to Each experiment was repeated 10 times with different random seeds, and the average results are reported. Further details of implementation are described in Appendix B
Baseline Methods The TSB-AD benchmark provides a comprehensive comparison across a total of 40 baseline methods, including 25 statistical and machine learning methods, 8 neural network- based methods, and 7 Transformer-basee methods. We additionally include 8 methods for further comparison with recent advances: PatchTST (Nic et al. 2023), DLinear (Zeng et al. (023)), NLinear (Zeng et al. 2023). DCdetector (Yang et al.) 2023). rrrnsfformrr (Liu et al. 2024). CATCH (Wul ett al. 2025). KAN-AD (Zhou al.. 2025), ) and DADA (Shentu al. 2025) Among total of 48
baseline methods, 39 are applicable to univariate time-series anomaly detection and 31 to multivari-
time-series anomaly detection. The hyperparameters of all baseline methods were tuned in the
same way as for the proposed method. The search spaces used are provided in Appendix B
Performance Measures For reliable and comprehensive evaluation, we employed three range- wise and three point-wise measures, respectively. The range-wise measures comprise the VUS-ROC and VUS-PR (Paparrizos et al. [022] Liu & Paparrizos 2024, Boniol et al. (025), together with the Range-wise Fl score (Range-Fl). As point-wise measures, we adopt AUC-PR, AUC-ROC, and Point-Fl. Following the TSB-AD (Liu & Paparrizos 2024), VUS-PR was regarded as the primary evaluation measure, while the others were used as complementary measures. Detailed definitions of the measures are presented in Appendix C
4.2 RESULTS AND DISCUSSION
Anomaly Detection Results Tables and summarize the performance of the proposed method and competitive baselines on the univariate (TSB-AD-U) and multivariate (TSB-AD-M) datasets from the TSB-AD benchmark. We report the top-2 performing methods based on VUS-PR, along with those published within the past 4 years (since 2022) for category except for Transformers. The full experimental results and statistical tests for all compared methods are provided in Appendix
In univariate time-series anomaly detection (Table PaAno ranked first across all six performance measures, outperforming all baseline methods. The qualitative results in Figure show that PaAno effectively captures diverse types of anomalies, ranging from abrupt point anomalies to contextual anomaly segments. Among the baselines, KAN-AD, which belongs to the neural network- based methods, achieved the second-best results across all six measures. Statistical and machine learning
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Experimental results on univariate time-series anomaly detection TSB-AD-U. For each measure, the best and second-best values are indicated in bold and underlined. All scores are reported with their rankings as "ScoreRRank".
Table 2:
Range Wise Measure"
Point Wise Measure
Computationa Cost
Method
VUS-PR
VUS-ROC
Range-F1
AUC-PR
AUC-ROC
Point-Fi
Params
Run Time
(Sub)-PCA
201
0.42a
0.769
0.41a
0,37
0.71m
0.42s
SaikML
KShapeAD
0.404
0,769
0.404
0.35M
0.74k
0.39
8.0s
DLinear
2024
0.25mm
0.74m1
0.22no+
0,21
0.62
0.26:a1-
0.1M
2.9
Nlincar
0.23mt
0.72mu-
0.20m
08
0.62ar
0.23am-
0.1M
DeepAnnT 2018
0.34ns
0.79
0.35s
0.33s
0.7lm
0.388
0.1M
2.0g
USAD 20
0.36nD
0.7
0.32n
0.662n
0.37%
1.76
TimesNer 2022
0.26
0,72
0.21/204
0. 0.18
0.61.20
0.24
0.1M
11.2s
FITS 20225
0 0.26/20
0,73
0.20rz0+
0.17
0.61.a
0.23/mn-
3.18
DADA 2025
0.3
0.77/
0.3
0.29t14
0.71m
0,38m1
184M
0.86
KAN-AD 2025
0.43re
0.82/2
0.43r
0.412
0.80vz
0.44k2
0.1M
12.15
Anomaly Trans
0.1220.
0.56.2n-
0.14
0.08224
0.50
0.12
L8M
48.9%
DCdetector 2025
0.09
0.56.m-
0. 100se
0.05pm0
0,50ka
0,100
0.9M
5.8s
Lag-I.lama
0.72nm-
0.25mm
0.65.m-
0.30mm-
1220.8c
Trandormer
OFA (20238
0.24
0.71
0.20r20
0.16600
0.59k20
0.22
81.9M
171.18
PatchTST 22023
0.26
0.75m1
0.22:24
0,21:20+
0.63/20-
0.25/20-
0.5M
26.3s
iTransormer
2024
0.22
0,74.19
0.18
0. 0.166
0.61.2n-
0.21:20-
0.6M
9.8s
MOMENT (FT)
2114
0.39
0.76/
0.35w
0.30m>
0.69ns
0.35m
109.6M
43.6s
MOMENT (ZS)
0.38g
0.75a7
0.367
0.30vz2
0.68nc
0.35mz
109.6M
42.9s
TimesFM 2024
0.30ns
0.74gp
0.34:14
0,28/17
0.67/19
0.34n6
203.5M
83.8s
PaAno (Ours)
0.52/1
0.48/1
0.46/1
0.86/1
0.51/1
0.3M
6.95
Experimental results on multivariate time-series anomaly detection in TSB-AD-M. For each measure, the best and second-best values are indicated in bold and underlined. All scores are reported with their rankings as "Score/Rank".
Table 3:
Range- Wise Measure
Point- Wise Measure
Computational Cost
Method
VUS-PR
VUS-ROC
Range-FI
AUC-PR
AUC-ROC
Point-Fl
Params
Run Time
KMeansAD
2001
0.29k1
0.73a
0.33e
0,25mA
0.69%
0.31ns
62.0s
Shnk
PCA 2
0.31a
DLincar
2023
0.29m1
0.70az
0.26v15
0.27g
0.66112
0.32m
< 0.1M
14.8
NLinear
2023
0.29k1
0.70vz
0.28mz
0.24ns
0.65/14
0.31/1
0.1M
15.0s
DocpAn'T
0.31a
0.762
0.374
0.32a
0.37B
Omniinonnial
2019
0.31a
0.69ts
0.374
0,27g
0.65r14
0,32
0.1M
9.1s
0.19mt
0.64mm-
0.17m
0.13m
0.56mm-
0.20cm-
FITS 2223
0.21m2
0.66.20
0. 16
0.15m
0,58k2
0,22
0.1M
16.78
DADA 2025
0.31a
0.73is
0.25v1s
0,3
0.69ks
0.35%
184M
2.18
KAN-AD 2025
0.41e
0.75%
0.41n
0.38:
0.73/2
0.42/2
0.1M
31.9%
Anomaly Transformer 2021
0.12
0.57
0.14.20
0.07:204
0.52
0.12/20-
L8M
55.8s
TransSemee
PatchTST (2023
0.28ns
0.71.
0.26v15
OFA (2023h
0.21mm
0.63mm-
0.17/
0.1mm
0.55mam-
0.21,m-
81.9
532.9%
DCdelector 2023
0.10cm
0.562
1.10000
0.06.20+
0,50cm
0. 0.10
15.0s
iTransforme: 2024
0.29k1
0.70xz
0.23x20
0.23x14
0.63120
0.28ns
0.6M
24.46
CATCH 2025
0.30s
0.73is
0.27/14
0.24ns
0.67/
0.30ne
210.8M
40.1s
PaAno (Ours)
0.44//1
0.79/1
0.41/1
0.38/1
0.76/1
0.43/1
0.3M
12.8%
methods overall achieved competitive performance relative to those in the other categories, despite their simplicity. Particularly, (Su recorded modest scores for four measures including VUS- PR. Transformer-based methods showed relatively low performance despite their heavier architec- tures.
In multivariate time-series anomaly detection (Table 3). PaAno again ranked first across all six per- formance measures. Among the basclines, several neural network-based methods showed superior performance to other methods. KAN-AD achieved the second-best VUS-PR score, while DADA, DeepAn'T and OmniAnomaly ranked third. Among statistical and machine learning methods, PCA also ranked third in VUS-PR. comsformer-basee methods again showed relatively low performance.
Discussion The results show that introducing an inductive bias toward locality and explicitly mod- eling short-range temporal dependencies was highly effective and efficient for time-series anomaly detection. Rather than focusing the processing time-series in a sequential manner, PaAno treats them as collections of temporally structured patches, enabling it to capture even subtle deviations from normal patterns and thereby achieve superior performance in anomaly detection.

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Published as a conference paper at ICLR 2026
Top-k (TSB-AD-U)
(TSB-AD-M)
Bank (TSB-AD-U)
Bank Size (TSB-AD-M)
08
64
622
52
320
44
25
420
20
40
114
200
107
48
40
0.3
81
44
30
46
30
10%
20%
17
5%
10%
Sampling Ratio (36)
Sampling Ratio (%)
VUS-ROC ---
AUC-ROC
WUS-PR
AUC-PR
Range-F1
Point-F1
Figure 5: Sensitivity analysis on Top-k and memory bank size of PaAno across TSB-AD-U/M.
In practical deployments, the patterns of normal data may change over time. PaAno can address this with simple online update of the memory bank without requiring model retraining. By constructing the memory bank queue that inserts recent normal patch embeddings and discards old ones, it continually reflects up-to-date normal patterns and remains robust to non-stationary normal regimes.
Sensitivity Analysis We con- ducted a comprehensive abla- tion study to assess the con- tribution of each component in PaAno. Some important results are summarized in Table 4 Re- moving instance normalization and excluding either the triplet or pretext loss from the train- ing objective lead to substan- tial drop performance, indi- cating that each component con- tributes to the effectiveness of PaAno. The triplet loss, with the farthest patch in the embedding space used as the negative pair, consistently outperforms InfoNCE and other variants. While the pretext loss in PaAno is applied only during the early stage of training. altering its scheduling to use it in the later stage degrades performance and increases computational cost. detailed analysis of the ablation study is provided in Appendix D
Table 4: Ablation study on the core components of PaAno. The best values for each measure are indicated in bold.
VUS-PR
VUS-ROC
Rung-FF
AUC-PR
AUC-ROC
Point-FI
who InstonceNorm who Exprr and Cyein
46
65.0
-h
42.6
K2.5
47.3
45.
56.9
42.5
35.6
53.9
45.0
AD
web Negative Selection in Cagge
49.6
$7.2
47.0
44.5
$4.3
49.2
Replace Eipia with InfoNCE loss
48.4
M63
45.3
42.6
K3.5
47.8
who Eqemm Cuztinant Use of Eyuacs who Linear Decur on Epezes
49.5
F8.2
46.6
43.6
85.4
49.2
46.8
$7.1
45.3
40.4
$4.0
45.8
50.7
53.4
45.2
45.6
K5.4
50.7
PaAmu (Thrm)
$1.9
18.6
4506
KS.7
S8.7
weo
who Eorps Grron
Nageine Replace Exgua with InfoNCE
37.0
32.8
nooo
38.8
36.2
Chustinan Use of Exgncs who Lincar Dasay
40.2
76.6
39.6
35.4
73.1
40.0
43.0
79.1
40.6
38.1
76.0
42.9
Padkoo (Ouss)
43.1
79.3
41.4
38.3
76.1
43.0
We also demonstrate that PaAno is robust to its hyperparameters. as it maintains stable performance across different settings. Figure 5 shows the results obtained by varying the memory bank size and the number of nearest neighbors used in anomaly scoring. PaAno further shows robustness to other hyperparameters, including the patch encoder architecture, loss weight, patch size and minibatch size. Full results are reported in Appendix D
Run Time We measured the average run time of each method across the datasets within each benchmark. As shown in Tables PaAno showed highly competitive run time, highlighting its practical efficiency for real-time applications. While majority of recent Transformer-base required significantly longer run times due to their heavy architectures and resource demands, PaAno was substantially faster with superior performances. Detailed results are provided Appendix E
basclines
5 CONCLUSION
We proposed PaAno, a simple yet effective method for fast and efficient time-series anomaly de- tection. Instead of relying on heavy model architectures, PaAno employs a lightweight ID-CNN to map time-series patches into vector embeddings and leverages patch- representation learning through metric learning and pretext task. We evaluated PaAno on the TSB-AD benchmark, which offers a rigorous evaluation protocol with performance measures that exclude point adjustment and threshold tuning. PaAno consistently achieved oftte-of-the-an performance compared to existing methods in both univariate and multivariate anomaly detection. Its architectural simplicity and com- putational efficiency make it well-suited for actual deployment in real-world industrial applications.
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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This work was supported by the National Research Foundation Korea (NRF) grant funded by the Korea govermment (MSIT; Ministry of Science and ICT) (No. RS-2023-000277903)
ETHICS STATEMENT
This work solely proposes time-series anomaly detection method. It does not involve any human subjects or personally identifiable information, and we consider the risk of misuse of this work to be low.
REPRODUCIBILITY STATEMENT
Our code is fully included in the submitted source files. To aid reproducibility, all hyperparameters and environmental details used in this paper are provided in Appendix B. All search space for other compared methods also fully provided in Appendix B. All datasets used in this study are publicly accessible, and all information about them is contained in this paper.
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7ahra Zamanzadeh Darban, Geoffrey Webb, Shirui Pan, Charu Aggarwal, and Mahsa Salehi. Deep learning for time series anomaly detection: A survey. ACM Computing Surveys, 57(1):15, 2024.
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. Are transformers effective for time series forecasting? In Proceedings of the AAAI Conference Artificial Intelligence, volume 37, pp. 11121-11128, 2023. doi: 10.1
Qianyu Zhou, Jiaxi Chen, Han Liu, Shuyu He, and Weizhu Meng. Detecting multivariate time series anomalies with zero known label. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 19663-9971 2023a. doi:
Quan Zhou, Changhua Pei, Fei Sun, Jing Han, Zhengwei Gao, Haiming Zhang, Gaogang Xie, Dan Pei, and Jianhui Li. KAN-AD: Time series anomaly detection with Kolmogorov-Arood net- works. In Proceedings of the International Conference Machine Leurning, 2025.
Tian Zhou, Peng Niu, Liyuan Sun, and Ruiyang Jin. One Fits All: Power general time series analysis by pretrained LM. In Advances Neural Information Processing Systems, volume 36, pp. 43322-43355, 2023b.
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A PSEUDOCODE
Algorithm D presents the pseudocode the training procedure. docode of the anomaly detection.
Algorithm
presents the pseu-
Algorithm Training Procedure of PaAno
Input: Time-series training dataset X (X XN)
Output: Trained patch encoder fo
<- (pr (Xe, ................................ Xt+ (e-t--1)]cs 1)]2=1" w++1
2: fo f- initialize patch encoder
3: + initialize projection head
4: initialize classification head
5: for iteration
Titer do Sample minibatch {p.) from
7: for each anchor patch p. E 5 do
8:
h. < fp.)
9:
2; (h:)
10:
P. < positive patch of P. from
11:
P. farthest negative patch of p. from (p:}
12:
z + go(fo(p,))
2. so([((p)) ))
14:
P < preceding patch of p, from P
15:
{PTS) J==1 random patches from B {p:}
gandn
16:
h.
<- [o(P,"
17:
h + fo( rand E E U}
18:
end for
19:
Lini N7 Ed max dist(z;, 2t1 dist( 2, - 6)
20:
Cpre
ΣM [ log co (h,, h,T0) 1 2n log(1 co co(h, hc h))]
21: Update fe. go. and cæ to minimize Lui + A Epre
22:
end for
23: return fo
Algorithm Anomaly Detection Procedure of PaAno
Input: Trained patch encoder fo, Reduced memory bank M, Time-series dataset X test (x), -xx,,..;xN')
Output: Anomaly scores {s1,)8 J.
1: for t.
N' do
2: P.
p: (xi, (xi,..., 1) w 1....,to]
3:
S._
5
--
0
4:
for each p. E Pt. do
h: +- fo(pt)
{m]
m, (k) + select the nearest neighbors he cosine distance from M
7:
S(p.) D dist(h, (())
S.. + +S. (S((p.)
9:
end for
10:
P.. Eippess. S(p.)
11: end for
12: return (st, )b
B IMPLEMENTATION DETAILS
B. IMPLEMENTATION DETAILS OF PAANO
The patch encoder fo was a ID-CNN consisting of four 1D convolutional layers with kernel sizes [7, 5, 3, 3) and channel dimensions [128, 256, 128, 64), each followed by batch normalization and ReLU activation. A global average pooling layer was applied after the final convolutional layer to obtain 64-dimensional patch embedding. The projection head go two-layer MI.P with ReLU
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activation in the first layer. Both layers had dimensionality of 256. The classification head cæ was a one-layer MLP with sigmoid activation.
We adopted instance normalization (Kim et al. 2022b) following a widely used convention in recent time-series anomaly detection (Yang et al.J 2023; Wu et al. 2025) and forecasting methods (Jin et al. 2024 Wang al. 2024). For the hyperparameters, the maximum offset for defining positive patches was set to 2, and the margin for the triplet loss was set to 0.5. The number of per- anchor random patches U was to 5. The model was trained for 200 iterations using the A optimizer with a minibatch size M of 512 and a weight decay of le-4. The learning rate was decayed to one-tenth of its initial value using a cosine annealing scheduler. The pretext loss weight À was linear decayed from 1 to 0 during the first 20 iterations and fixed at 0 thereafter. The memory bank size was set to 10% of the original patch set P. The number of nearest neighbors k in the anomaly scoring function was set to 3. The patch size wo and initial learning rate were explored from (32, 64, 96} and {1e-3, 1e-4, le le-5), respectively. based on VUS-PR performance on the Tuning split of the TSB-AD benchmark. A patch size of 64 and learning rate of 1e-4 were selected for TSB-AD-U, and 96 and le-4 for TSB-AD-M.
Experiments were conducted using an NVIDIA RTX 2080Ti GPU with 11GB of memory. Each experiment was repeated 10 times with different random seeds, and the average results are reported.
B.2 HYPERPARAMETE TUNING FOR BASELINE METHODS
Among the 46 baseline methods, 37 are applicable univariate time-series anomaly detection and 29 to multivariate detection. The hyperparameters of the baseline methods were tuned in the same manner as PaAno, using VUS-PR performance on the Tuning split the TSB-AD benchmark (Liu & Paparrizos, 2024). For the baseline methods included in TSB-AD, we adopted the best hyper- parameter settings reported for their search spaces in the benchmark. For the remaining baseline methods, we conducted hyperparameter searches by defining comparable search spaces. The com- plete search spaces for all baseline methods are summarized in Tables 5andd 6
C EVALUATION OF TIME-SERIES ANOMALY DETECTION
C.I CHALLENGES IN EVALUATION PRACTICES
The recent studies on time-series anomaly detection have often relied on evaluation protocols that in- troduce several biases, undermining the validity of reported results (Liu & Paparrizos 2024 Sarfraz al. 2024).
First, several commonly used benchmark datasets exhibit known structural flaws (Liu & Paparrizos, 2024]. A primary issue is mislabeling, where inconsistencies in labeling lead to some anomaly- labeled observations being indistinguishable from normal patterns. Another common issue is unre- alistic assumptions about anomaly distributions, such as assuming that anomalies occur only once or appear only at the end of a time series. These flaws compromise the reliability and validity of evaluations.
Second, the reliance performance measures that use point adjustment and threshold tuning has created an illusion of effectiveness for sophisticated methods. Point adjustment (Kim et al. 2022a) treats an entire anomaly segment correctly detected if even single point within the segment is detected. While originally intended to address temporal misalignments and noisy labels, this
misleadingly inflate performance measures (Sartraz et al. 2024] BBhattcharya al.] 2024). Threshold tuning is typically performed post-hoc and tailored to each method (Paparrizos et al.)
2022 Liu & Paparrizos) 2024. Sarfraz et al. (024)). Customizing the threshold selection strategy for each method can probably lead to biased evaluations tailored to specific approaches. Also, determining a universal threshold is challenging due to varying periodicities and variances in time- series data.
C.2 TOWARD MORE RELIABLE EVALUATION
We adopt the TSB-AD benchmark LLu & Paparrizos 2024). grounded in recent rigorous studies on time-series anomaly detection. This benchmark mitigates dataset-related flaws by correcting la-
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Table 5: Hyperparameter search spaces for 38 univariate time-series anomaly detection methods.
Category
Method
Hyperparameter
Hyperparameter
DLinear
win_size: [60, 80, 100]
None
[Forest
n_estimators: [25, 50, 100, 150, 200]
None
KMeansAD
clusters: [10, 20, 30, 40]
win size: [10, 20, 30, 30,44) 40]
KShapeAD
periodicity: 2,.
None
LOF
n.ncighbors: [10, 20, 40,
metric: [minkowski, manhattan, euclidean]
MatrixProfile
periodicity: [1, 2,
None
NLinear
win_size: [60, 80, 100]
None
periodicity:
power:
[1, 2,3,4
Stutistical & Machine Learning
SAND
periodicity: [1. 2.
None
Series2Graph
periodicity: [1. 2,
None
SR
periodicity: [1.
None
(Sub)-HBOS
periodicity: 2]
n_bins: 10, 30,
(Suh) IFores IForest
periodicity: [1, 3]
n_estimatons: [25, 50, 100, 150, 200]
(Sub)-KNN
periodicity: 2,
n neighbors: [10, 20, 30, 40, 50]
(Sub)-LOF
periodicity: 21,
n_neighhors: 20, 30, 50]
(Sub)-MCD
periodicity: 2..
support fraction: [0.2, 0.4, 0.6, 0.8, None]
(Sub)-OCSVM
periodicity: (1.
kernel: [lincar. poly, rbf, sigmoid]
(Sub)-PCA
periodicity: [1. 2,
n.components: [0.25, 0.5, 0.75, Nonc]
AutoEncoder
[50, 150]
hidden meurrus: [[64, 32). [32, 16). (128, 64]]
DADA
batch size: [32, 64, 96]
None
DeepAn'T
win_size: (50, 100, 150]
num.chammel: [32, 32, 40). [16, 32, 64]]
Donut
win_size: [60, 90, 120]
Ir: [0.001, 0.0001, 1c-05]
FITS
win_sire: [100, 200]
Ir: [0.001, 0.0001, 1e-05]
Neural Network
Conventional
KAN-AD
win_size: [32, 64, 96]
Ir: [0.01, 0.001, 0.001,0.0001
LSTMAD
win_size: [50, 100, 150)
Ir: [0.0004, 0.0008]
OmniAnomaly
size: [5, 50, 100]
Ir: [0.002, 0.0002]
TimesNet
win_size: [32, 96, 192]
Ir: [0.001, 0.0001, 1c-05]
TranAD
win_size: [S. 10, 50]
Ir: [0.001, 0.0001]
win_size: [5, 100]
Ir: [0.001, 0.0001, 1e-05]
AnomalyTranee Transformer
[50, 150]
Ir: [0.001, 0.0001, 1c-05]
Chronos
win.size: (50. 100, 150)
None
DCdetector
win_size: [80, 100]
Ir: [0.0001.
iTransformer
win_size: [64, 96]
Ir: [0.0001,5ee (05
Transformce
Lag-Llama
win_size: [32, 64,
None
MOMENT (FT)
win_size: [64, 128, 256]
None
MOMENT (ZS)
size: [64, 128, 256]
None
OFA
win_size: [50, 100, 150]
None
PatchTST
num.epoch: [5. 10, 15]
None
TimesFM
win_size: [32, 64, 96]
None
Ours
PaAno
patch size: [32, 64, 96)
Ir: [0.001, 0.0001, 1c-05)
beling inconsistencies and reflecting realistic anomaly distributions. It also systematically addresses issues such as point adjustment and threshold tuning, enabling fair and consistent evaluation.
Regarding performance measures for time-series anomaly detection, Paparrizos et al. (2022) pro- posed the VUS of the Precision-Recal Curve (VUS-PR) and the Receiver Operating Characteristic curve (VUS-ROC) as robust and lag-tolerant measures. with resilience to temporal misalignment and noise. Building upon this, Liu & Paparrizos (2024) empirically validated VUS-PR as the most fair and reliable measure, capable of jointly capturing detection accuracy and localization quality at the segment level. [Sarfraz et al. (2024) recommended the joint use of Point-F1 and Range-Fi, both of which are bareshold-dependen measures, and suggested supplementing them with the Area Under the PR Curve (AUC-PR) as threshold-independenn measure. Additionally, the Area Under the ROC Curve (AUC-ROC) remains one of the most widely used measures for providing a global view of ranking performance, despite being sensitive random scores in highly imbalanced set- tings (Paparrizos et al. 2022 Liu & Paparrizos) 2024). Overall, employing multiple complementary measures is essential for comprehensive and reliable evaluation.
Following this guidance, we adopted six performance measures in this study: VUS-PR, UR-P VUS-ROC, and Range-Fl range-wise measures; and AUC-PR, AUC-ROC, and Point-Fl wise mea- sures. Four measures excluding Range-F1 and Point-Fl Seint-F--serve serve as prrehhod-independeee mea- sures, and point adjustment was not applied to any of the measures.
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Table 6: Hyperparameter search spaces for 30 multivariate time-series anomaly detection methods.
Category
Method
Hyperparameter
Hyperparameter?
CBLOF
n_clusters: 8 16, 32]
alpha: [0.6, 0.7, 0.8, 0.9]
COPOD
None
None
DLinear
size: [60, 100)
None
EIF
n.trees: [25, 50, 100, 200]
None
HBOS
bins: 15, 10, 20, 30, 40)
tol: 10.1, 0.3, 0.5, feauurrt [0.2. 0.4, 0.6, 0.8, window size: 110, 20, 30, 40]
Forest
n.estimators: [25, 50, 100, 200]
Statitical &
KMeansAD
clusters: [10, 20, 30, 40]
Machine Learning
KNN
n.ncighbors: [10, 20, 30, 50]
method: [largest, incan, median]
LOF
neighbors: [10, 20, 30, 50]
metric: [minkowski, manhattan, euclidean] Nome
MCD
support Fraction: [0.2, 0.4, 0.6, 0.8, None]
NLincar
win.size: [60, 100)
None
OCSVM
kernel: [linear, puly, rbf. sigmoid]
nu: 10.1, U.S, 0.7]
PCA
a.components: [0.25, 0.5, 0.75, Nonc]
None
RobustPCA
Hter: 1500, 1000, 1500)
None
AutoEncoder
win size: [50, 100, 150]
hidden ncurons: [[64, 332, 16), [128, 64]l
DADA
butch [32, 64,
Nome
DocpAnT
win size: [50, 100. 150]
num channel: [[32. 32, 40). [16, 32, 641)
Donut
win size: [60, 120]
Ir: 10.001. 0.9001, 1e-05)
FITS
win_size: [100, 200]
[0.001, 0.0001, 1c-05]
Conventional
Neural Network
KAN-AD
win size: [32, 64, 96]
Ir: [0.01, 0,001, 0.0001|
[50,
150]
[0.0004, 0.0008)
OmniAnemaly
win size: [5, 50, 100)
Ir: [0.002, 0:0002]
TimesNet
win size: [32, 96, 192]
Ir: [0.001, 0.0001, 1e-05]
TranAD
win.size:
50]
[0.001, 0.0001]
USAD
win size: [5, 50, 100)
Ir: [0.001, 0.0001, 1e-05]
win size: 150, 100, 150]
Ir: 10.001. 0,0001, 1e-05)
CATCH
patch [16, 32,
[0.0001, Sc-05]
DCdetector
win size: 180, 100)
Ir: [0.0001. 1c-05]
Transfommer
iTransformer
164,
[0.0001, 05
OFA
win_size: [50. 100, 150]
Nonc
PatchTST
num num,epooe epoch: [5, 10, 15]
Nome
patch size: [32, 64,
1c-05]
C.3 DETAILS OF PERFORMANCE MEASURES
Notations Let Let = (XI, .,, ) denote the test dataset consisting of N' time steps, where each X, I2d represents d-dimensional observation at time Let y, {0, 13 denote the corresponding ground-truth label, where y/- = 1 indicates an anomaly and z/c = 0 otherwise. Let E IR denote the predicted anomaly score at time and (T) 1(s 7) the binarized prediction obtained by thresholding the score at threshold r.
The Receiver Operating Characteristic (ROC) curve plots the true positive rate (TPR) against the false positive rate (FPR) as the threshold varies. The TPR and FPR at threshold r are defined as:
AUC-ROC
TPR(7) =1=1 (6(T) Vt.
FPR(T) E1= úe (T) N ΣM (1 y/)
ve)
E Dt
Given finite set of thresholds {T1, . Tk sorted in descending order, the Area Under the ROC Curve (AUC-ROC) is computed as:
K-1
Σ (FPR(T)) FPR(T++))) TPR(Te) + TPR(T&+1)
AUC-ROC-
2
AUC-PR The Area Under the Precision-Reccd Curve (AUC-PR) summarizes the trade-off be-
tween precision and recall across varying thresholds:
AUC-PR Σ (B((T)) R((++1)) P(Tx) P(TR+1)
K-1
2
where P(r) and R(r) denote the precision and recall at threshold 7, computed
M 1 De(T) Ve.
N² 1= De(T)
P(7)
R(7)
ΣN 1= 1(s 7)
ΣN 1=1 31
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Point-F1 The standard point-wise Fl score (Point-FI) is defined the maximum FI score all possible thresholds T:
Point-F1 max 2. ((())
P(r) + R(T)
Range-F1 An anomaly segment is defined as contiguous subsequence of time steps labeled as anomaly. Let A , denote the set of ground truth segments, where Aj = {t aj Σ t S by. y/ = 13. For a given threshold т. let A(r) = (AI...., AN. denote the set of predicted segments, where A; {t às st st hi (c(T)) = =}.
Range-P(r)) HA,- A(T) 3A, A, A. #0H,
IA(T)
Range-R(r)) HAj EAI 3/, E A(T),, A₁ n n * 0)
[
Then the range-based score is:
2 Range-P(rrT ((r) Range-R(r)
Range-F1 = max
Range-P(1T + Range-R(T)
VUS-ROC
The Volume Under the ROC Surface (VUS-ROC) extends the standard ROC-AUC to mire-diimessional evaluation by jointly varying the threshold the lag tolerance l around labeled anomalies. ROC curves are computed for all possible pairs (T, (). where E {....... FK } and l (O...., L.,, forming surface. VUS-ROC is then defined as the volume under this ROC surface.
Following the TSB-AD benchmark (Liu & Paparrizos 2024), we use the optimized implementation of VUS proposed by [Paparrizos al.) (2022). The maximum lag tolerance L is determined by identifying the first prominent local maximum in the autocorrelation of the first channel of the time series, which typically corresponds to the dominant repeating interval.
VUS-PR Analogous to VUS-ROC, the Volume Under the Precision-Reaall Surface (VUS-PR) constructs surface by varying both the threshold = and the lag tolerance É over PR curves. The VUS-PR is defined as the volume under this surface, providing a threshold-inde ieseeedd-nddeeendeett and lag- tolerant generalization of the standard PR-AUC.
D SENSITIVITY ANALYSIS
We conducted comprehensive sensitivity analysis validate the effectiveness and robustness of the core components of PaAno.
D.1 PATCH ENCODER ARCHITECTURE
The patch encoder fe PaAno primarily adopts a simple ID-CNN as the default in this study, but it can be flexibly extended to various other architectures. We conducted experiments comparing it with more complex encoders, including ID-ResNet (Wang et al. 2017). Temporal Convolutional Network (TCN) (Bai et al., 2018)) and OmniScaleCNN (OmniCNN) (Tang et al. (022). which were implemented using the tsai package (Oguiza 2023). To analyze the architectural sensitivity of the patch encoder, we also evaluated variants of the default ID-CNN (4-layer, 1x width) by modifying its width (0.5x and 2x) and depth (3-layer and 5-layer) while keeping the kernel configurations unchanged.
Table 7 summarizes the results and the number parameters of each model, with the simple ID- CNN highlighted in bold the default setting. In the TSB-AD-U, the lighter variants of the ID- CNN, those with reduced width (0.5x width) or depth (3-layer), achieved performance comparable to or slightly exceeding that of the heavier variants, despite having fewer parameters. Consequently, for univariate anomaly detection, a lighter ID-CNN offers an efficient and effective choice with- compromising accuracy. In contrast, in the TSB-AD-M, heavier variants (2x width & 5 layer)
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Table Sensitivity analysis on the patch encoder architecture. The best and second-best values for each measure are indicated in bold and underlined, re- spectively, All values are reported in pereentage (%).
Range-FI
ID-CNN (0.5x width)
147K
51.5
$8.5
47.9
45,5
85.8
50.6
ID-CNN layer)
297K
150 0
ID-CNN
371K
$1.9
88.6
48.2
45.6
85.2
50.7
ID-CNN 1S layer)
1126K
50.8
88.4
47.9
44,4
85.6
49.8
ID-CNN
1250K
OmaiCNN
401K
52.2
$3.2
49.0
46.1
85.2
$1.1
TCN
655K
46.4
85.5
45.4
41.6
83.0
46.3
ID-CNN (0.5x widhh)
162K
41.8
78.9
39.8
37.1
75.6
42.0
ID-CNN (3 layer)
318K
42.6
79.4
42.3
37.6
76.2
42.6
TSU AD M
ID-CNN
386K
43.1
79.3
11,4
38.3
43.0
ID-CNN (5 layer)
1148K
43.2
78.8
41.5
38.1
75.6
42.9
ID-CNN (2x width)
1326K
43.5
41.9
75.8
43.2
DmmiCNN
968K
40.9
79.0
38.2
35.5
75.8
40.2
TCN
66-K
37.9
80.3
44.2
35.9
79.0
43.0
ID-ResNet
1485K
41.5
40.7
36.7
75.9
42.1
showed slightly stronger performance than the default model. However, lighter variants still retained competitive performance, indicating that PaAno is robust width and depth variations within the ID-CNN architecture.
For other architectures, OmniCNN achieved higher performance than the ID-CNN in TSB-AD- U, whereas in TSB-AD-M its performance was lower despite having roughly two to three times more parameters. TCN exhibited superior performance on ROC-based measures (VUS-ROC, AUC- ROC), but showed relatively low performance on TSB-AD-U despite having nearly twice as many parameters as the default ID-CNN. ID-ResNet yielded comparable performance in TSB-AD-U, but comparatively lower performance in TSB-AD-M, while requiring nearly four times more parameters than the ID-CNN. These results show that the ID-CNN, while having the smallest number of param- eters among the different architecture, performs comparably across both univariate and multivariate anomaly detection tasks.
D.2 LOss FUNCTION
Table 8: Sensitivity analysis of the triplet loss compared with the InfoNCE loss and different negative selection strategies. The best and second-best values for each measure are shown in bold and underline, respectively. All values are reported in percentage (%).
function
Negative Sampling
VUS-PR
VUS-RDC
Range-F1
AUC-PR
AUC-ROC
Point-FI
TSB-AD AD-MA TSB-AD-U
InfaNCE
All Non-Self Panch
86.7
45.3
42.6
83.6
47.8
Tripket Triplct
Random
49.6
87.2
47.0
44.5
84.3
49.2
Tripler Triplet
Median
45.5
84.9
42.9
39.7
81.3
45.0
Farthest
$1.9
88.6
48.2
45.6
85.7
50.7
InfoNCE
Noo
Tripler
Random
36.9
76.7
35.1
32.4
73.5
37.2
Triplct
Closest
41.0
78.6
39.2
36.7
75.4
41.3
Triplct
Triplet
Parthest
43.1
79.3
41.4
38.3
76.1
43.0
Table 8 reports the sensitivity analysis of the triplet loss in PaAno compared to InfoNCE and other negative sampling strategies. Since PaAno extracts patches through a sliding window, there is no guarantee that other patches in the minibatch form semantically meaningful negatives for given anchor. This lack of semantic correspondence makes many InfoNCE negatives ambiguous, which weakens the contrastive signal and destabilizes the embedding space (Wang & Dou 2023).
We demonstrated other ways to choose the negative patch to analyze how the negative selection and compared the patch
the triplet loss in PaAno respond different forms of contrast. Specifically, farthest from the anchor in the embedding space (Farthest), the patch closest to the anchor (Closest), randomly chosen patch (Random), and the patch the median of the similarity ranking (Median).
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In the experiments, median negatives offered weak contrast compared to random negatives or InfoNCE in TSB-AD-U, and performed comparably to them in TSB-AD-M. Random negatives showed highly similar behavior to InfoNCE, providing consistent contrast. These results suggest that random or percentile-based negatives fall short of providing the meaningful contrast required for effective metric learning of time-series patches. In contrast, the default strategy in PaAno, the farthest negative consistently achieved the strongest performance. It is shown to offer reliably dis- tinct comparison that encourages the encoder learn discriminative representations. Interestingly, the closest negative also performed second best in both univariate and multivariate settings. These results indicate that metric learning for time-series patches is most effective when the negative is clearly dissimilar to the anchor, and still reasonably effective when it is clearly similar, whereas intermediate or ambiguous negatives provide weaker contrast.
Table 9: Sensitivity analysis of the loss weight À with respect to the applied ratio and the scheduling strategy. Here, each ratio (e-g.. 10%, 50%, 100%) denotes the proportion of the initial part of training during which the pretext loss is applied. For a specified initial ratio the training iterations, Linear decays linearly to zero, whereas Constant keeps it fixed. The best and second-best values for each measure are shown in bold and underline. respectively. All values are reported in percentage (%).
Ratio
Schedule
VUS-PR
VUS-ROC
Range-F1
AUC-PR
AUC-ROC
Point-F1
Linear
51.9
88.6
48.2
45.6
50.7
TSB-AD-M TSB-AD-U
10%
Constant
50.7
88.4
47.0
44.7
85.6
49.7
50%.
I Linear
48.6
87.5
46.6
42.2
84.5
47.0
50%
Constant
48.1
87.3
45.5
41.7
84.3
46.7
100%
Linear
46.8
87.1
45.3
40.4
45.8
100%
Constant
46.6
86.7
45.7
40.6
83.6
45.6
10%
Linear
43.1
79.3
41.4
38.3
76.1
43,0
10%
Constant
43.0
79_1
40.6
38.1
76.0
42.9
Linear
40.1
77.9
39.8
41.0
50%:
Constant
39.4
77.2
38.8
35.0
74.0
40..
100%
Linear
40.0
76.6
39.6
35.4
73.1
40.0
100%
Constant
39.2
76.7
38.3
34.9
Table 10: Sensitivity analysis of the loss weight X. The best and second-best values for each measure are indicated in bold and underline, respectively. All values are reported in percentage (%).
A
VUS-PR
Range-Fi Fl
AUC-PR
AUC-ROC
Point -l
TSB-AD-M TSB-AD-U
0.1
51.5
88.7
45.1
85,9
0.5
50.9
88.4
47.0
44.9
50.0
1.0
51.9
88.6
48.2
45.6
85.7
50.7
2.0
$1.0
88.5
47.2
45.1
85.6
50.1
0.1
43.4
79.9
42.0
38.5
76.7
43.2
0.5
43.1
79.2
41.0
38.6
76.0
43.1
1.0
43.1
79.3
41.4
38.3
76.2
43.0
2.0
43.0
79.3
40.7
38.5
76.1
43.1
The pretext loss in PaAno is applied only during the early iterations to stabilize representation learn- ing when the embedding space is still unstructured and triplet-based distance comparisons are un- reliable. Using this auxiliary objective in the initial phase improves training stability and shows consistent performance gains (Table 4.. Additionally, to analyze how the pretext loss should tegrated with the triplet objective, we conducted sensitivity analyses on its application strategy and loss weight.
Table reports the effect of the ratio of initial iteration the pretext loss applied with scheduling methods. Applying the pretext loss only in the initial 10% portion of training yielded the best over- all performance, while extending it beyond that ratio led to progressively loss is shown to interfere with triplet-based discrimination once the embedding space becomes more structured. Also, linear-decay schedule, which gradually reduces the pretext loss instead of remov-
results. The pretext
22

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Published as a conference paper at ICLR 2026
ing it abruptly, consistently outperformed constant schedule across all ratios. These results shows the effectiveness of the default application strategy for pretext loss in PaAno.
Table 10 further analyzes the loss weight. The performance remains stable across range of weights, indicating that PaAno is robust to the choice of the loss weight.
D.3 MEMORY BANK SIZE
Sensitivity analysis on the size of the memory bank, expressed as a percentage of the training dataset size. The best and second-best values for each measure are indicated in bold and underlined, respectively. All values are reported in percentage (%).
Table 11:
Size
VUS-PR
VUS-ROC
Range-FI
AUC-PR
AUC-ROC
Point-F1
1%
50.6
88.1
46.8
44.8
85.2
49.9
TSB- A-3-AD-M TSB-AD-L
5%
52.0
88.4
48.0
45.8
85.5
50.8
%
51.9
88.6
48.2
45.6
85.7
50.7
20%
51.0
88.5
47.3
45.0
85.6
50.1
1%
42.9
78.6
40.6
38.4
75.4
43.0
5%
43.1
38.6
76.0
43.2
%
43.1
76.1
43.0
20%
43.2
79.4
38.6
76.3
43.2
Table 11 presents the results of sensitivity analysis the size of the memory bank M, with the 10% highlighted bold as the default setting. Varying the memory bank size had no significant impact performance. Since PaAno maintains its effectiveness (e.g., 1%), the size can be reduced from the default setting when fast inference or memory efficiency required.
with small memory bank
D.4 NUMBER OF NEAREST NEIGHBORS USED IN ANOMALY SCORING
Table 12: Sensitivity analysis on the number of nearest neighbors retrieved from the memory bank for anomaly scoring. The best and second-best values for each measure are indicated in bold and underlined, respectively.
k
VUS-PR
VUS-ROC
Range-Fi
AUC-PR
AUC-ROC
Point-F1
1SB-AD-M TSB-AD-U
51.1
88.5
47.2
45.1
85.7
50.0
51.9
88.6
48.2
45.6
85.7
50.7
50.9
88.3
47.1
45.0
85.5
50.0
46.9
44.9
85.2
49.8
43.1
79.4
40.6
38.6
76.3
43.1
79.3
41.4
38.3
76.1
43.0
79.0
40.9
75.8
42,9
10
41.8
78.6
40.3
37.4
75.5
42.1
Table 12 presents the results of sensitivity analysis on the number nearest neighbors i retrieved
from the memory bank M for anomaly scoring, with the highlighted in bold as the default setting. The results show that performance remained stable & varied. This suggests that PaAno robust to the choice of & around the default setting. Although PaAno shows stable performance with k=1, we adopt k=3 as the default to enhance stability against potential noise.
D.5 PATCH SIZE
presents the results of the sensitivity analysis patch size. The results show that perfor- mance remains stable as the patch size varies. In both TSB-AD-U and TSB-AD-M, PaAno achieves strong performance at patch size of 32 and shows slightly improved performance from 64 on- ward. Patch sizes of 64 and 96 were selected for TSB-AD-U and TSB-AD-M, respectively, based results from the TSB-AD-TTning
Table 13
23

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Published as a conference paper at ICLR 2026
Table 13: Sensitivity analysis on the size of patch length. The best and second- best values for each measure are indicated in bold and underlined, respectively. All values are reported percentage (%).
Size
VUS-PR
VUS-ROC:
Range-F1
AUC-PR
AUC-ROC
Point-Fi
TSB-AD-M (SB-AD-U
32
42.9
85.4
41.1
36.9
81.1
42.9
64
51.9
88.6
48.2
45.6
85.7
50.7
96
52.9
89.2
48.9
47.5
87.0
52.2
128
53.6
88.6
49.3
47.0
86.8
51.9
32
35.3
75.8
32.9
30.8
70.8
35.8
64
41.0
78.6
38.9
36.1
74.7
40.7
96
43.1
79.3
41.4
38.3
76.1
43.0
128
40.7
78.0
40.3
36.7
75.6
41.8
D.6
MINIBATCH SIZE
Table 14: Sensitivity analysis on the size of minibatch size. The best and second-best values for each measure are indicated in bold and underlined, re- spectively. All values are reported in percentage (%).
Size
VUS-PR
VUS-ROC
Range-FI
AUC-PR
AUC-ROC
Point-FI
ISB-AD-M TSB-AD-U
128
50.8
88.0
46.5
44.8
84,9
50.0
256
51.1
88.4
47_1
45.2
85,5
50.2
512
51.9
88.6
48.2
45.6
85.7
50.7
1024
50.8
88.2
46.8
44.8
85.3
49.9
128
42.3
79.0
39.9
37.8
75.9
42.3
256
42.7
79.3
40.5
38.1
76.1
42.8
512
43.1
79.3
41.4
38.3
76.1
43.0
1024
42.9
79.5
40.8
38.1
76.3
42.7
Table 14 presents the results of the sensitivity analysis on minibatch size. Unlike other contrastive losses (e.g., InfoNCE), the loss function in PaAno does not heavily depend on the minibatch size, because each anchor requires only a single farthest negative pair. Across both TSB-AD-U and TSB- AD-M, PaAno remains consistently robust for all tested minibatch sizes.
E DETAILED EXPERIMENTAL RESULTS
E. SUBSET-WISS WISE EXPERIMENTAL RESULTS PAANO
To ensure thorough and transparent evaluation, present the detailed subset-wise experimental results of PaAno on TSB-AD-U and TSB-AD-M in Table 15
E.2 ENTIRE EXPERIMENT RESULTS
We report the complete experimental results for 40 methods, including PaAno, on TSB-AD-U in Table 16 and for 32 methods on TSB-AD-M in Table I7
E.3
RUN TIME
To evaluate the practical applicability real-time anomaly detection, we measured the run time of each method, including both training and inference, averaged across the datasets within each benchmark. The results for the baseline methods are taken from the TSB-AD benchmark (Liu & Paparrizos 2024), where statistical and machine learning methods 7713 CPU. while neural network-based and Transformer based methods were run on an NVIDIA A 100 GPU. For PaAno, used NVIDIA RTX 2080 Ti GPU measure its average time.
exccuted on AMD EPYC
24

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Published as a conference paper at ICLR 2026
Table 15: Experimental results of PaAno by subset from ADB- and TSB-AD-M benchmarks.
Name of Stivuti # Time-Suiis
Avg. Dim
VUS-PR
VUS-ROC
Range-Fi
AUC-PR
ADC-ROC
Point-Fi
0.9
0.6755100134
101155
YAHOO
30
0.6521
0.9537:00001 0013
0:44030
0,5566:10 0097
0.0515500000
0,509 D066
0,9012000019
60453-00000
0.6058
0,5122+00013 0126
0.929000000466
0.5214-0.0033
CATSV2
1
0.3019200045
0.7465:300000 0010
0.1551=000006
0,40000 0004
0.7089
0.5234
Daphace
0,3763
69171
0.3192+00550
0.5737
0.9234-000231
04399-0.0316
Exatloo
30
1
0.7700000000
0:9476:200003
05267
0.7090:100000 00B5
0.9448:500003
0.79020000101
JOPS
0.0022
0,9066.1000083
0.4226 1. 0091
0,25801100000
L0.0015
0.3462L0.0006
LTDB
0.7233:0000044
0.8357:100008 0050
0.7185::00044
0.6532:10 0041
0. 7564:0000000
0.0034:000108
MGAD
0.10151000010
0,9341 19341100.001
0,41455L0 000
0.2616.10.002
0.921810.0025
0.3746
MITDB
0.0018:000000
0.8461:00000 0020
0.3136::0.009
0,2798:10000 0001
0.7892:0000020
0.5438:00000
TSB. AD
MSI.
0.2766+000220
0,7638+00028 0262
5:00072
0.2295+000011
0, 0.70466
00.012
NAB
0.003
0,7768:10 0055
0.5083:000006
0:4574100000 0026
0:00022
0.5044:000077
NEK
0,991 3-00012
0 0014
0 01007
0,5467+000000
0. 000000
0:600400.002
OPPORTUNITY
0.2477
6.6700:000131
0.25
0.6713=00130
0.5233=00004
Power
0.16700000002
06437100007
0,2270.000162
0.17511000002
0.0231
0 0.209610.0002
SED
0.96245000057
0:90000000001
08018:000444
0.00
0.99
0
SMAP
0,7807 1:0001
0.9156.1000002
0,7634100002
0.7745:10:0088
0,9129
0.7632.000043
0,5015100151
0,91241100002
0.54261000100
0,4741:10.022
0.5076.10:0122
Stockk
D. 2071 +00000
0.8407:00000 0003
1.1601 000066
00774+00000
0
D. 0.1505=0.000 0001
0.5696.1000
0,9133110.0011
0.6085100002
0.5172.10.0016
0,9021000000
0,5466.00000
SWAT
0.0947=00000
0. 17666:00 0006
0. 0.1001+00000
0.0044+00000 0006
0.1806000000
0.2154:00000 D002
0.8737
0:967:000015
0.2888=00711
0:1366:00.003
0,5205
0.013
TOD8
0.7794+000000
00113+400000
0.3303+0.0100
0.8222+000066
0.7034+0.000
0.9077-0.0099
Average
0,5188 100003
0.886310.0024
0,4819 100000
0,455910000
0,857310.0022
0,5068.
CATS+2
17
100007
0001R1000000
0.1419.100011
0:0448110013
0,0858
0.130120:0145
CreditCard
0.0222100002
0,525410.000
0.0270100014
0.0050.1000027
0,5126100.1177
0.002310.0000
Dophact
9
0.2788=00550
B. 0184
0.3899000140
0:003+000009
0.9075-0.0346
14211:0.0044
Exablas
20.16
0.8135
00041=000078
0.682=00043
0.785880.00344
0,95175000055
0.8535=0.0256
GFCCO
9
0. 1866+000522
0.8088+000259
0.2600000000
0.2319+00050
0.0191
0.3144-0.0519
Genesis
18
0,3334 2009
0991990000444
0.5791=01336
0.2766640 2681
0.9906
0,3854=0.200 2030
GHL
19
0.00851000002
0,3339:000111
0,0204
0.0072.0.0002
0.3000:000111
0,02
TSB- AD-M
LTDB
2:25
0,6221 1000000
0,8663.100000 0069
0.01931000128
0.11111001199
0.826710.0051
0.000200.006
MITDB
2
0.57662:000044
0.8766.100008 DIES
00000
0.4187:10.0107
0.4784 0.8826-0000036
MSL
55
0.0451
0,7818110.0147
0.3780L000228
0,201811000314
0,7569 10.0144
0.298210.0299
OPPORTUNITY
248
0. 1607:000533
0.6044:0000
0.4426:0.0000
0.1541 010.333
0,5823:0.0322
0. 0,0090:0000 0283
PSM
25
0.20171000022
0.652110.016
0.27281100007
0,1836.100 1..00.0
10.001
0,2643
SMAP
25
0:5119:00017
0.8970: 0006
0.5720:00011
9005:000177
0,901 :00.0073
0.5060=000127
SMD
38
0,3578=000155
6.894+00000 0015
0:4099000054
6.3041+000305
0.8738=0:0044
044000000010
SVDB
0.56162000008
6:094:0.0117
0,5492:600000
0.5306:60.0006
0.8799000008
0.559
SWNT
58.5
0:2461+000228
66811006322
0:0411
0,2198+000000
0.6662=0.0400
0.3147=0.0299
0,72676000121
6,5255000000
02:10:000000
0004
0.4250
0.1593=000008
TSR-AD-M Average
0,4307-8.8025
0.7928-980022
0.4141=00000
0.3530+880022
0,7613-8.1027
0.4299-0.0029
Figures 6 and 7 compare the average run times of the methods on TSB-AD-U and TSB-AD-M. PaAno exhibited competitive run time, averaging 6,8508 seconds on TSB-AD-U and 12.8393 sec- onds on TSB-AD-M. PaAno achieved faster run time than majority of Transformer-based methods, demonstrating superior computational efficiency despite its competitive performance. Statistical and machine learning methods and generally required less run time, demonstrating their utility for scenarios demanding low-latency or contrained-resoure usage.
Compared to baseline methods, PaAno's 's advantage in run time becomes more pronounced on TSB- AD-M. While the run time of baseline methods typically increases with the number of channels in the time series, the run time of PaAno primarily depends on retrieving the nearest patch embeddings from the memory bank and is thus highly robust to the number of channels.
E.4 SCORE DISTRIBUTION
Figure presents the distribution of VUS-PR scores for each method across all time series in the TSB-AD Eval set. The methods are ordered from left to right based on their average VUS-PR scores, and both the median and mean values are visualized to reflect central tendency and consistency. Among them, PaAno exhibited the highest median and mean VUS-PR scores in both univariate and multivariate time-series anomaly detection. The gap between the median and mean was also the smallest, indicating that PaAno achieved stable performance distribution.
25

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Published as a conference paper at ICLR 2026
Table 16: Performance comparison 40 univariate time-series anomaly detection methods on TSB- AD-U.
Category
Method
VUS-PR
Rank
VUS-ROC
Rank
Range-F1
Rank
AUC-PR
Rank
AUC-ROC
Rank
Point-F1
Rank
(Suby-PCA
0142
01.76
D141
0.37
0.71
0.42
KShapeAD
0.35
0.74
SenessGrupb
0.39
0
0.35
0.33
0.76
KMeamAD
0.37
0.76
9
0.38
0.32
0.74
5
0.37
ManriProffile
0.35
11
0.76
0.32
17
0.26
20
0.73
8
0.33
18
SAND
0.34
13
0.76
0.36
0.29
14
0.73
Stiistical
SR
0.32
16
0.81
5
0.35
0.32
0.74
5
0.38
Mochine Leaming
iForest
0.30
18
0.78
7
0.50
21
0.29
14
1.71
0.35
12
(Subs-LOF
D.16
27
(Suh)-MCD)
0.24
30
0.72
26
1.24
27
D.15
36
0.67
0123
29
NLineas
11.23
32
0.72
26
1.20
34
0.18
27
0,62
29
01.23
29
(Sub-HBOS
24
0.18
(Sub) [Forest
0.22
35
0.72
26
0.23
28
0.16
0.63
0.22
LOY
0.17
28
0.68
35
0.22
30
0.14
37
0.58
36
0.21
36
KAN-AD
0,43
0.82
D
0,41
0.80
0.44
10
DeepAnT
0.34
L3
0.79
0.35
0.33
0.71
LSTMAD
0.33
15
0.76
0.34
14
0.31
10
0.68
16
0.57
Conventional
Neural Nerwork
OmniAnomaly
DADA
0.31
17
I.77
D.51
19
0.29
14
I.71
OL33
18
FITS
0.26
23
0.73
0.20
34
0.17
30
0.51
0.23
29
TimesNer
0.26
23
0.72
26
0.21
33
0.18
27
061
31
0124
27
0.20
MOMENT (FT)
0.39
0.76
0.35
0.30
12
0.69
0.35
12
MOMENT (2S)
0.38
0.75
17
0.36
0.30
12
0.68
16
0.35
12
TimesFM
0.30
18
0.74
0.34
14
0.28
17
0.67
0.34
16
Tranffrmer
Log-Lllamo
PatcbIST
0.26
23
0.75
17
0.22
30
0.21
25
0.63
0.25
24
OFA
0.24
30
0.71
32
1.20
34
0.16
31
0.59
0.22
11
TTranforrmr
Anomaly Transfomer
DCdetector
0.09
40
0.56
39
0.10
40
0.05
40
0.50
0.10
40
Ours
PuAso
0.52
0.89
0.48
0,96
0.85
0.51
Average Run Time (Seconds)
107
361
10*
SR
POLY
iForest
Sub-HBOS
Jub-IForest
Sub-PCA
DADA
USAD
DeepAnT
Sub-MCD
AutoEncode:
OmniAnomaly
DLinear
FITS
LSTMAD
Series2Graph
KShapeAD
DCdetector
NLinear
PaAno
Donut
TranAD
iTransformer
TimesNet
KAN-AD
KMaansAD
MatrixProfile
Sub- OCSVM
SAND
PatchTST
LOF
MOMENT (ZS)
MOMENT (FT)
AnomalyTranss Traansformer
Chronos
Sub-LOF
KNb-KNN
TimesFM
OFA
Lag-Llama
Figure 6: Average time TSB-AD-U.
26

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Published as a conference paper at ICLR 2026
Table 17: Performance comparison of 32 multivariate time-series anomaly detection methods on TSB-AD-M.
Method
VUS-PR
Rank
VUS-ROC
Rank
Rank
AUC-PR
Rank
AUC-ROC
Rank
Point-Fi
Rank
DLincar
0.29
11
0.70
0.26
13
0.27
0.56
KMeumAD
0.29
11
0.73
6
0.33
0.25
14
0.69
6
0.31
13
NLiness
0.29
11
0,70
0.28
12
0.34
15
0.65
0.31
13
0.27
16
0.20
26
0.27
0.85
Stristical
OCSVM
0.26
18
0.67
22
0.30
10
0.23
18
0.11
23
0.78
18
Mochine Leaming
RobustPCA
0.24
20
0.61
28
0.53
0.24
15
0.58
25
0.29
17
BIF
19
0.20
21
IForest
0.20
24
0.69
0.24
19
0.19
22
0.66
0126
22
HBOS
0.19
26
0.67
22
D.24
19
0.16
24
066
21
0.24
24
0.14
KAN-AD
0.41
0.75
0.41
0.38
0.73
0.42
DADA
0.31
0.73
0.25
18
0.31
0.69
0.35
DompAnT
0.31
0.76
0.57
0.32
0.73
0.37
LSTMAD
Neural Network
Conventional
AutoEacader
TISAT)
OmniAnomaly
0.31
0.50
0.30
0.68
0.69
0.69
21
16
D.57
0.37
0.28
12
0.30
0.27
0.26
12
0.64
0.67
0.65
8
0.34
0.31
13
25
TimesNet
0.19
26
0.64
0.17
27
0.13
0.56
0.20
28
TraND
0.18
28
0.65
25
0.21
23
0.14
27
0.59
24
0.21
26
CATCH
0.30
0.73
0.27
L4
0.24
15
0.67
8
0.30
16
Transfomerr
OFA
PachTST
0.28
0.21
21
15
0.71
0.63
27
0.26
0.17
27
13
0.26
0.15
25
12
0.65
0.55
28
0.32
0.21
26
Anomaly Transfumerr
0.12
31
0.57
0.14
30
0.07
0.52
31
DCdettettr
Average Run Time (Seconds)
10a
107
10*
PCA
HBOS
Forest
COPOD
CBLOF
MCD
DADA
USAD
OmniAnomaly
DeepAnT
OCSVM
AutoEncoder
PaAno
DLinear
NLinear
DCdetector
FITS
LSTMAD
TranAD
Trransformer
KAN-AD
CATCH
EIF
eromaalyransformer
TimesNet
LOF
RobustPCA
KNN
Donut
KMeansAD
PST
OFA
Figure 7: Average run time on TSB-AD-M.
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---

Published as a conference paper at ICLR 2026
TSB-AD-U
TSB-AD-M
KAMAD
Sub-PCA
KShapsAD
POLY
Seriest
Sub-oWn
MatroProftic
DesplenT:
MOMENT
Figure 8: Boxplot of VUS-PR distributions for the TSB-AD-U and TSB-AD-M. The dashed line and solid line represent the mean and median values, respectively. Only the top-12 12 methods ranked by average VUS-PR are presented, ordered from left to right accordingly.
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Reviews:
[{"review_id": "uR28lcxskh", "reviewer": "Reviewer_6WeB", "summary": "This paper presents PaAno, a lightweight and effective method for semi-supervised (normal-only) time-series anomaly detection. The authors argue that recent large-scale models (e.g., Transformers) provide diminishing returns for high computational costs, often failing to outperform simpler methods under rigorous evaluation protocols. PaAno challenges this trend by proposing a compact 1D-CNN architecture based on patch-based representation learning.\n\nThe model is trained on normal data by extracting short temporal patches. A patch encoder (1D-CNN) is optimized using a dual loss function:\n\nTriplet Loss: A metric learning objective that clusters temporally similar patches in the embedding space.\n\nPretext Loss: A self-supervised classification task that predicts whether two patches are temporally consecutive.\n\nAfter training, embeddings of normal patches are stored in a compressed memory bank. During inference, the anomaly score of a new patch is computed as its distance to the nearest neighbors in this memory bank, effectively measuring its dissimilarity from all learned normal patterns. Experiments on the rigorous TSB-AD benchmark show PaAno achieves state-of-the-art (SOTA) performance, ranking first across all six evaluation metrics for both univariate and multivariate data, significantly outperforming heavier Transformer-based baselines.", "questions": "The paper's justification is that \"local patterns matter,\"  but this is not a new insight. Simple baselines like (Sub)-PCA also operate on local windows yet perform significantly worse (Table 2). Conversely, large Transformers are fully capable of learning local patterns but also fail. This implies the success is not just about \"being local.\" What specific properties does the 1D-CNN encoder learn from the patches—as a result of this specific dual-loss training—that (Sub)-PCA fails to capture and that Transformer-based models apparently miss?\n\nThe paper combines Triplet Loss and a Pretext Loss, both of which are well-established techniques . The ablation study (Table 4) shows both contribute. However, what is the specific synergistic effect between these two? For instance, does the pretext task (predicting temporal consecutiveness) primarily help structure the embedding space for the triplet loss to find more meaningful negatives? Or are their contributions merely additive? How critical is this specific pretext task, or would any generic self-supervised task have achieved a similar outcome?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The method is architecturally simple, utilizing a lightweight 1D-CNN (0.3M parameters)  and achieving a fast runtime (Tables 2 & 3). This makes it a practical solution for resource-constrained environments, which is a commendable engineering goal.\n2. The authors have conducted a comprehensive evaluation on the TSB-AD benchmark, adhering to its rigorous protocols. The inclusion of a proper ablation study (Table 4) and hyperparameter sensitivity analysis (Fig. 4) meets the requirements of a solid experimental paper.", "weaknesses": "Significant Lack of Novelty: This is the primary flaw of the paper. The proposed method is highly incremental and appears to be a straightforward combination of well-established, existing techniques.\n\n- The core idea of \"patch-based representation learning\" for anomaly detection is directly borrowed from the computer vision domain.\n\n- The use of 1D-CNNs for time-series feature extraction is standard.\n\n- The use of Triplet Loss and self-supervised pretext tasks  are both common, off-the-shelf methods for representation learning.\n\n- The paper fails to demonstrate a novel conceptual contribution. It reads more like an application of a known (visual) anomaly detection recipe to the time-series domain, rather than a new method developed from first principles.\n\nBesides, the paper's main justification is that \"local patterns matter\" and that large Transformer models are \"inefficient\". These are not new insights. The paper fails to provide a deep analysis of why this specific combination of old techniques so dramatically outperforms other methods (including simpler ones like (Sub)-PCA ) on this benchmark. The impressive empirical result lacks a correspondingly strong conceptual or theoretical justification.", "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": "lntl4i3xVG", "reviewer": "Reviewer_AfnK", "summary": "This paper proposes a patch-based temporal anomaly detection method that integrates memory mechanisms and representation learning to accomplish the task of temporal anomaly detection. The introduction and motivation are relatively clear and well-defined.", "questions": "1. The author needs to clearly and repeatedly clarify their contributions, especially regarding the motivation for integrating previous technologies, and whether there is truly an element of lightness, which requires stronger theoretical and experimental evidence; the presentation also needs to be adjusted. \n\n2. It is recommended that the author supplement with more comparative work from the past 25 years, as the current comparison algorithms are insufficient.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 1, "strengths": "1. The motivation and ideas of the article are very clear, and the introduction is relatively clear; \n\n2. The article uses more and broader evaluation metrics, rather than flawed point adjustment metrics; \n\n3. Time series anomaly detection is a field worth exploring and has certain practical application value; \n\n4. The article's figures are well-made, clearly presenting the content intended to be conveyed.", "weaknesses": "1. The novelty of the paper is limited. There has been much discussion about the Patch mechanism and Patch sequentiality in temporal tasks and temporal anomaly detection tasks, and the introduction of a memory mechanism in TSAD is not a novel contribution. The statements at the beginning of the paper are insufficient to demonstrate the novelty of the proposed framework. \n\n2. The comparative baselines in the paper are weak. Targeting ICLR 2026, it seems that many recent SOTA methods are missing from the comparisons, especially those from 2025, which are few. It is recommended that the authors include more strong baselines to substantiate the effectiveness of their method. \n\n3. The claimed contribution of the method in terms of lightweight design is questionable, as the results on TSB-AD show that the proposed method is slower in terms of time consumption compared to many existing methods, making it difficult to demonstrate an advantage in training and inference.", "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": "U5BfqPN8PF", "reviewer": "Reviewer_2Foy", "summary": "The paper presents PaAno (Patch-based Anomaly Detection), a lightweight and effective framework for time-series anomaly detection. The core idea is based on patch-based representation learning, where the time series is divided into overlapping temporal patches that are embedded using a convolutional encoder. After training, embeddings of normal patches are stored, and during inference, anomaly scores are computed as the average distance between new patch embeddings and their nearest neighbors in the memory bank", "questions": "* Sensitivity beyond reported ranges for patch size, lag tolerance, and neighbor count. Are there regimes where PaAno fails (e.g., nonlocal anomalies or regime shifts)? \n\n* Any leakage from pretext construction across windows, and how is it prevented at train/test boundaries? \n\n* Why was triplet loss chosen over other metric objectives (e.g. Contrastive)? Any stability/convergence comments? \n\n* What are the hardware and exact runtime measurement details (GPU/CPU model, batch sizes during inference, parallelization)? Add them to the appendix or a short paragraph. \n\n* How does PaAno behave under non-stationary normal regimes (drift)? Any mechanisms (or easy extensions) for updating the memory bank online?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper presents time-series anomaly detection (TSAD) method by adopting the TSB-AD benchmark and employing lag-tolerant, threshold-independent metrics (with VUS-PR as the primary measure). It introduces a lightweight patch-encoder combined with metric learning and a memory-bank kNN anomaly scoring approach, applicable to both univariate and multivariate data. The emphasis is on robustness and efficiency rather than architectural complexity.\n\n* Paper combines simple components (1D-CNN, triplet loss, adjacency pretext, memory bank + kNN) yet yields strong empirical gains across multiple range- and point-wise metrics and both U/M benchmarks. The result that such a compact model outperforms many large transformer/foundation approaches under the TSB-AD protocol is interesting and practically relevant.", "weaknesses": "* Novelty is incremental. The components are known, the value lies in the clean, effective combination and rigorous evaluation. \n\n* There is little theoretical analysis explaining why triplet + adjacency pretext together produce the observed gains, or conditions when patch locality will fail (e.g., very long-range contextual anomalies). A short analysis or toy case study would strengthen the paper\n\n* All main results use TSB-AD (albeit a rigorous benchmark). It remains unclear how PaAno performs when anomalies are primarily global (long temporal context) or when the training set has non-stationary normals. Clarification on this can further strengthen the paper. \n\n* The ablations show triplet vs. pretext importance and sensitivity to k/memory size,. It would be great to further explore: (a) patch length w effects across dataset types, (b) encoder depth/width tradeoffs, (c) alternative metric losses (contrastive. etc ). These would clarify design choices.", "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": "v8Tt6xyqwp", "reviewer": "Reviewer_EcX6", "summary": "This paper proposes PaAno (Patch-based representation learning for time-series Anomaly detection), a lightweight framework for semi-supervised time-series anomaly detection. Instead of using large transformer or foundation models, PaAno applies a 1D-CNN to extract vector embeddings from short overlapping temporal patches. The model is trained with a triplet loss (for metric learning) and a pretext loss (for predicting temporal adjacency between patches). A memory bank of normal patch embeddings is built for inference, where anomalies are identified via distance to nearest normal embeddings. Evaluated on the TSB-AD benchmark, PaAno achieves state-of-the-art results across univariate and multivariate datasets, outperforming heavier architectures while remaining computationally efficient. Ablation and sensitivity analyses confirm the contributions of both loss terms and robustness to hyperparameters.", "questions": "What is the motivation behind decaying the weight for pretext loss?\n\nIn section 3.5, a visual representation of even a pseudo code of the anomaly detection method would aid in clarity.\n\nIs the sampling of negative patch done at inference time during the forward loop?\n\nWhat is the embedding space that is used to select a negative sample? Is it the embedding space of the mode? If so how is the initialization handled when the model is not trained?\n\nIn section 3,3, i need some clarification. I think there is an error. THe paper states “ We define the negative patch pi as the hard negative, chosen as the patch in the minibatch B that has the smallest cosine distance to pi in the embedding space, i.e.,\npj ∈ B \\ {pi } that minimizes dist(fθ (pi ), fθ (pj )).“ Shouldnt the distance be maximized here to find a negative patch that is the most dissimilar to the patch p?\n\nI am willing to improve the rating if my questions are answered.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "Originality: Fair\n\nQuality: Good\n\nClarity: Fair\n\nSignificance: Good\n\n\nAdditional note: The paper is mostly well written and the results are extremely promising. The methodology is clearly presented and mostly easy to follow. The methodology is an extension of existing research but is applied to a new domain in a clever and relevant way.", "weaknesses": "Hyper-parameter tuning: The paper claims the model is resilient to hyper-param tuning, but the paper does not show the affect of tuning the hyper params in triplet loss and combined loss. Additionally, as the triplet loss samples the current mini batch at inference time, would the mini batch size not be a big hyper-param design consideration?\nTraining details: There is no motivation given to as why the pretext loss weight decays to 0 within the first 20 iterations, this is in the appendix, I believe this is a major detail that needs to be discussed as the paper claims that the pretext loss is essential for the model performance.\n\nGeneralization to other datasets: Only one dataset was used to evaluate methods.\n\nFormatting/Editing Mistakes: Table 10 in Appendix E3 shows a row called Total , but it shows (what i assume is) the mean of the different metrics in the table.\n\nLimited theoretical grounding: The paper lacks formal analysis explaining why the patch-based embedding space generalizes effectively to unseen anomalies.\n\nUnsubstantiated assertions: In section 3.1, the paper asserts that “Modelling long sequences with heavy global attention can dilute these local temporal dependencies”. This is not backed by a citation or theoretical or empirical proof. Additionally the term “heavy” here is ambiguous.", "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)