Title: Self-Perturbed Anomaly-Aware Graph Dynamics for Multivariate Time-Series Anomaly Detection
Venue: NeurIPS 2025
Accepted: yes
Keywords: Anomaly Detection

Time-Series Analysis

Graph Neural Networks

Spatio-Temporal Graph
Abstract: Detecting anomalies in multivariate time-series data is an essential task across various domains, yet there are unresolved challenges such as (1) severe class imbalance between normal and anomalous data due to rare anomaly availability in the real world; (2) limited adaptability of the static graph-based methods to dynamically changing inter-variable correlations; and (3) neglect of subtle anomalies due to overfitting to normal patterns in reconstruction-based methods. To tackle these issues, we propose Self-Perturbed Anomaly-Aware Graph Dynamics (SPAGD), a framework for time-series anomaly detection. SPAGD employs a self-perturbation module that generates self-perturbed time series from the reconstruction process of normal ones, which provide auxiliary signals to alleviate class imbalance during training. Concurrently, an anomaly-aware graph construction module is proposed to dynamically adjust the graph structure by leveraging the reconstruction residuals of self-perturbed time series, thereby emphasizing the inter-variable disruptions induced by anomalous candidates. A unified spatio-temporal anomaly detection module then integrates both spatial and temporal convolutions to train a classifier that distinguishes normal time series from the auxiliary self-perturbed samples. Extensive experiments across multiple benchmark datasets demonstrate the effectiveness of SPAGD compared to state-of-the-art baselines.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors proposed self-perturbed anomaly-aware graph dynamics (SPAGD), a novel TSAD framework that addresses several crucial challenges in multivariate time-series anomaly detection. Specifically, SPAGD employs self-perturbations derived from reconstruction errors to generate auxiliary anomalous time-series data to facilitate the training of detection model. An adaptive anomaly-aware graph construction module is proposed to adjust graph structures according to the dynamic disruptions within time-series data, and a spatio-temporal modelling framework is integrated to achieve anomaly detection. Experiments conducted on well-established benchmark datasets validate that SPAGD significantly surpasses multiple state-of-the-art baseline methods.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. The self-perturbation strategy is a novel and promising solution to the scarcity issue of anomalous data in TSAD. By perturbing normal data, the proposed method generates an evolvable set of auxiliary anomalies to enhance the detection capability of the model without requiring labelled anomalies, which is a common bottleneck in the field.\n2. The proposed anomaly-aware dynamic graph construction allows the model to adjust the learned graph structure based on real-time reconstruction residuals. This makes it more adaptable to capture shifting dependencies in real-world applications with complex, evolving systems compared to static graph construction methods.\n3. The theoretical analysis in the appendix well justifies the rationale of the self-perturbation and graph dynamics components modules.\n\n**Weaknesses**\n1. The description of the self-perturbation module lacks sufficient detail on how the Transformer-based reconstruction model is initialized and how the perturbation evolves during training. For instance, the paper does not clarify whether the reconstruction model is pre-trained or trained from scratch alongside other components, which impacts reproducibility.\n2. The claims of performance superiority would be more convincing if supported by statistical significance testing. I suggest the authors conduct appropriate statistical tests (e.g., t-test) to validate that the improvements over key baselines are statistically significant.\n3. This paper does not justify the rationale for selecting $k$-NN for initial graph construction. Can another alternative strategy be used for graph construction, and how could this impact the model performance? The authors need to elaborate on it.\n4. The computational complexity of the proposed methodology is not currently included. It is recommended that the complexity analysis be supplemented with a discussion of its feasibility for practical deployment.\n5. The statement of  \"no societal impact\" in the checklist is oversimplified for a technology with broad potential applications. I encourage the authors to at least give a brief discussion covering both positive societal benefits and potential negative risks.\n\nQUESTIONS:\n1. Can the authors provide more details on how the Transformer-based reconstruction model was initialized?\n2. What is the rationale for selecting $k$-NN for initial graph construction? How would other alternative graph construction strategies affect the performance?\n3. What is the computational complexity of the proposed method? Is it comparable to other baseline methods?\n4. Are the performance improvements of SPAGD compared to other baselines statistically significant? Can the authors further conduct statistical significance tests to demonstrate this?\n5. Can the authors briefly discuss about the social impact of this work?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a for GNN-Transformer-based framework named Self-Perturbed Anomaly- aware Graph Dynamics (SPAGD) for time series anomaly detection problems. The model consists of two components: a Transformer-based autoencoder and a GNN-based spatiotemporal anomaly predictor. The authors first feed the raw sequence into the autoencoder to obtain a reconstructed sequence (referred to as Self-Perturbation in the paper), which is then passed to a GNN that operates on an adjacency matrix constructed from the reconstruction error (referred to as the Anomaly-Aware Graph). Meanwhile, the original sequence is processed by another GNN based on a static adjacency matrix. The outputs of both GNNs are fed into a prediction network to produce the anomaly scores. Notably, all neural networks are trained jointly. A number of experiments were conducted to verify the effectiveness of the method.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n1.\tThe writing is clear and easy to understand.  \n2.\tIn the experimental section of the paper, SPAGD demonstrates superiority over multiple state-of-the-art methods across three datasets and provides ablation study to prove the necessity of each module, showcasing high quality.  \n\nWeaknesses:\n1.\tThe main motivation stated in the Introduction is the anomaly-imbalance problem. However, the proposed method does not clearly explain how this issue is addressed, and there is a lack of corresponding experiments. Moreover, I believe that as long as the model effectively learns the distribution or features of normal data (without overgeneralizing), it should be sufficient to detect any abnormal behavior—especially given that the task focuses solely on determining the presence of anomalies, rather than identifying their specific types. Additionally, it is questionable whether the employed encoder-decoder network is capable of generating or mimicking unseen anomalies.  \n2.\tSetting aside some details (i.e., using a separate dynamic graph for the self-perturbated sequence), the overall approach resembles a standard GAN framework: a Transformer-based reconstruction network serving as the generator, a GNN-based anomaly scorer acting as the discriminator, along with an adversarial loss design. Given that GANs have already been widely applied to time series anomaly detection, this framework is not particularly novel. Moreover, I am skeptical about the effectiveness of the dynamic graph (See the Question 3), especially considering its higher computational cost.\n3.\tThe authors provide some theoretical analysis in the appendix, which is appreciated. However, I find it difficult to assess the practical significance of these results. Specifically, Theorem 1 presents an upper bound on the 1-Wasserstein distance between the distributions of reconstructed and anomalous samples, expressed as the sum of their reconstruction errors relative to normal samples. It is unclear how this bound translates to meaningful insights for anomaly detection, especially given that the reconstruction of anomalous samples is not explicitly considered. A similar concern applies to Theorem 2, where the layer-wise error bounds raise the question of how such perturbations actually affect detection performance in practice.  \n4.\tThe experimental section appears somewhat weak, as comparisons are conducted on only three datasets. I recommend including additional datasets such as SMD [1] and PSM [2] to enhance the comprehensiveness of the evaluation. Furthermore, a model complexity analysis would be beneficial—since the results of the ablation study may be affected by reduced parameter counts, the same concern applies to model comparisons.\n\nQUESTIONS:\n1.\tSee the \"Weaknesses\" section.  \n2.\tHow are the anomaly detection threshold selection and localization performed by the authors?  \n3.\tWhat would happen to the performance if the dynamic error graph were not used and the reconstructed sequence were processed with the static graph instead (which is more efficient)?  \n4.\tGiven the introduction of GNN to handle spatial relationships, how do the authors view recent time series forecasting works [3] that have found better regression results by not considering the dependencies (using channel-independence)?\n\nReferences:  \n[1] Su, Ya, et al. \"Robust anomaly detection for multivariate time series through stochastic recurrent neural network.\"  \n[2] Abdulaal, Ahmed, Zhuanghua Liu, and Tomer Lancewicki. \"Practical approach to asynchronous multivariate time series anomaly detection and localization.\"  \n[3] Nie, Yuqi, et al. \"A time series is worth 64 words: Long-term forecasting with transformers.\"\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses several challenges in multivariate time-series anomaly detection by proposing a novel approach called self-perturbed anomaly-aware graph dynamics (SPAGD). The core contributions include: (1) a self-perturbation module to generate diverse auxiliary anomalous data, mitigating class imbalance without external anomalous data; (2) an anomaly-aware graph construction to dynamically adapt the learned graph structure based on reconstruction residuals; and (3) a spatio-temporal model integrating both spatial and temporal information between time-series data to identify anomaly.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. The proposed SPAGD framework is novel and well-motivated, which addresses various challenges in time-series anomaly detection, including anomaly scarcity, adaptation to the changing interdependencies between variables, and anomaly reconstruction problems.\n\n2. The paper is well-structured with precise formulation and illustration. Additionally, the theoretical justifications for employing self-perturbations and dynamically adjustable graphs are compelling and insightful.\n\n3. Reproducible code is provided for enhancing both the reproducibility and credibility of this work.\n\n4. Experiments conducted across multiple real-world datasets provide empirical evidence to support the effectiveness of SPAGD. Further experimental analysis in the appendix offers more insights into the proposed framework.\n\n**Weaknesses**\n\n1. While the idea of this work is novel, the quality and diversity of the self-perturbed samples can be further discussed. The core assumption of SPAGD is that reconstruction residuals of a model can serve as ideal proxies for mimicking real anomalies. However, these \"anomalies\" are inherently tied to the model's own inductive biases. There may exist a potential risk that the model primarily learns to detect its own reconstruction artifacts rather than a general set of real-world anomalies. This could limit its generalization to real anomalies, as they may differ structurally from the generated perturbations.\n\n2. The additive update rule ($\\tilde{A}\\_{ij} = S\\_{ij} + \\sigma(r\\_{i})$) in the AAGC module is a key design. Can the authors provide more intuition or explanation for it? Have other alternatives been considered, such as multiplicative scaling? \n\n3. The evaluation relies on traditional point-wise metrics like AUC-ROC and F1-Score, which may not fully reflect the practical time-series anomaly detection performance. The authors should further add segment-based metrics such as the Volume Under the Surface (VUS) to provide a more robust assessment.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nYes, the authors have adequately discussed the limitations of this paper, particularly the assumption of homogeneous sensor data.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposed SPAGD for time-series anomaly detection. Specifically, SPAGD introduces the self-perturbation time-series generation, anomaly-aware graph construction, and spatio-temporal anomaly detection modules to correspondingly tackle three key challenges in the field: the imbalance between available normal and anomalous data, the limitation of static graph structures in capturing dynamic correlations, and the \"anomaly reconstruction\" problem. The entire framework is trained in an end-to-end way. The authors conducted comparative experiments on various benchmark datasets, showing that SPAGD outperforms a wide range of TSAD baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper is well-written and easy to follow. The problem statement is clear, and the technical details are explained logically. The related work section positions SPAGD well within the existing literature.\n2. Leveraging the evolving reconstruction residuals as a source of \"self-perturbation\" to generate training signals is novel and sound. This directly addresses the critical and practical challenge of data scarcity and class imbalance in TSAD without relying on external data or simple noise injections. This paper also identifies the \"anomaly reconstruction\" problem and proposes a promising solution to address it.\n3. The experimental results demonstrated that SPAGD significantly outperforms state-of-the-art TSAD baselines on various benchmark datasets, and the authors also provided the implementation code of SPAGD.\n\nWeaknesses:\n1. The paper adopts a \"chunk-wise\" temporal convolution, which breaks the continuity of the time series. What is the motivation for this design over standard sequential models (like LSTMs) that preserve temporal continuity? Does this approach potentially fail to capture dependencies that span across chunk boundaries?\n2. Although the authors have included several commonly used metrics in their evaluation, the TSAD community is increasingly adopting more robust metrics such as VUS-ROC and VUS-PR [1]. To strengthen empirical validation, I suggest the author supplement the evaluation of the proposed method using these two metrics.\n3. The hyperparameter $m$, which is the percentage of anomalous candidates, appears critical for the AAGC module, yet its impact is not analyzed. How does it affect the model's performance?\n4. The current ablation study is insightful, but the variant \"w/o SP\" removes both the auxiliary data and the classification objective. Would it be possible to conduct a more fine-grained ablation study to better isolate the contribution of individual components? For example, a variant that uses self-perturbation but with a reconstruction-based objective could help quantify the specific benefit of the classification objective.\n\n[1] Paparrizos et al. Volume under the surface: a new accuracy evaluation measure for time-series anomaly detection, Proceedings of the VLDB Endowment, 2022.\n\nQUESTIONS:\nThe authors should focus on addressing my concerns raised in Strengths and Weaknesses, which will affect my final assessment of this paper.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}