{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/time-series-anomaly-detection/papers/3","list_of":"/task/time-series-anomaly-detection","task":"Time Series Anomaly Detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":3,"rows_per_page":100,"rows":[201,264],"of":264,"counts":{"archive_papers_tagged":264,"with_a_code_link":127,"where_syntology_ran_a_sample":40,"not_listed_spam_title":0,"listed":264,"listed_where_code_ran":40,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":33,"every_run_a_failure_of_syntologys_instrument":7,"listed_with_a_run_with_no_instrument_failure":33,"listed_every_run_a_failure_of_syntologys_instrument":7,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/time-series-anomaly-detection","prev":"/task/time-series-anomaly-detection/papers/2","next":null,"papers":[{"url":null,"slug":"rola-a-real-time-online-lightweight-anomaly","title":"RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series","date":"2023-05-25","arxiv_id":"2305.16509","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-strategy-of-time-series-anomaly","title":"Evaluation Strategy of Time-series Anomaly Detection with Decay Function","date":"2023-05-15","arxiv_id":"2305.09691","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-deep-learning-libraries-on-online","title":"Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection","date":"2023-04-30","arxiv_id":"2305.00595","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-anomaly-detection-via-contextual","title":"Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection","date":"2023-04-16","arxiv_id":"2304.07898","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-domain-adaptation-for-time","title":"Context-aware Domain Adaptation for Time Series Anomaly Detection","date":"2023-04-15","arxiv_id":"2304.07453","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-anomaly-detection-based-on","title":"Time-series Anomaly Detection based on Difference Subspace between Signal Subspaces","date":"2023-03-31","arxiv_id":"2303.17802","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-byzantine-resilient-aggregation-scheme-for","title":"Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection","date":"2023-03-29","arxiv_id":"2303.16668","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bi-lstm-autoencoder-framework-for-anomaly","title":"A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset","date":"2023-03-17","arxiv_id":"2303.09703","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-representation-for-anomaly-detection","title":"Learning Representation for Anomaly Detection of Vehicle Trajectories","date":"2023-03-09","arxiv_id":"2303.05000","repositories_listed":0,"syntology":null},{"url":null,"slug":"repad2-real-time-lightweight-and-adaptive","title":"RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series","date":"2023-03-01","arxiv_id":"2303.00409","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-anomaly-detection-in-smart-homes","title":"Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach","date":"2023-02-28","arxiv_id":"2302.14781","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-time-series-anomaly-detection-2","title":"Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting","date":"2023-02-04","arxiv_id":"2302.02051","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-time-series-anomaly-detection-a","title":"Graph Anomaly Detection in Time Series: A Survey","date":"2023-01-31","arxiv_id":"2302.00058","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-efficient-interactive-time-series","title":"Label-Efficient Interactive Time-Series Anomaly Detection","date":"2022-12-30","arxiv_id":"2212.14621","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-anomaly-detection-in-time-series","title":"Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods","date":"2022-12-06","arxiv_id":"2212.03637","repositories_listed":0,"syntology":null},{"url":null,"slug":"lossy-compression-for-robust-unsupervised","title":"Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection","date":"2022-12-05","arxiv_id":"2212.02303","repositories_listed":0,"syntology":null},{"url":null,"slug":"mgadn-a-multi-task-graph-anomaly-detection","title":"MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series","date":"2022-11-22","arxiv_id":"2211.12141","repositories_listed":0,"syntology":null},{"url":null,"slug":"hfn-heterogeneous-feature-network-for","title":"HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection","date":"2022-11-01","arxiv_id":"2211.00277","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-on-unsupervised-anomaly","title":"A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis","date":"2022-09-10","arxiv_id":"2209.04635","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-baseline-network-for-time-series","title":"Deep Baseline Network for Time Series Modeling and Anomaly Detection","date":"2022-09-10","arxiv_id":"2209.04561","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-learning-of-deep-time-series-anomaly","title":"Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data","date":"2022-08-03","arxiv_id":"2208.01841","repositories_listed":0,"syntology":null},{"url":null,"slug":"causality-based-multivariate-time-series","title":"A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes","date":"2022-06-30","arxiv_id":"2206.15033","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphad-a-graph-neural-network-for-entity","title":"GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection","date":"2022-05-23","arxiv_id":"2205.11139","repositories_listed":0,"syntology":null},{"url":null,"slug":"lpc-ad-fast-and-accurate-multivariate-time","title":"LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding","date":"2022-05-05","arxiv_id":"2205.08362","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-deep-neural-networks-contribute-to","title":"Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?","date":"2022-04-04","arxiv_id":"2204.01637","repositories_listed":0,"syntology":null},{"url":null,"slug":"diverse-counterfactual-explanations-for","title":"Diverse Counterfactual Explanations for Anomaly Detection in Time Series","date":"2022-03-21","arxiv_id":"2203.11103","repositories_listed":0,"syntology":null},{"url":null,"slug":"stacked-residuals-of-dynamic-layers-for-time","title":"Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection","date":"2022-02-25","arxiv_id":"2202.12457","repositories_listed":0,"syntology":null},{"url":null,"slug":"recurrent-auto-encoder-with-multi-resolution","title":"Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection","date":"2022-02-21","arxiv_id":"2202.10001","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-anomaly-detection-for-time-series-data","title":"Robust Anomaly Detection for Time-series Data","date":"2022-02-06","arxiv_id":"2202.02721","repositories_listed":0,"syntology":null},{"url":null,"slug":"little-help-makes-a-big-difference-leveraging","title":"Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection","date":"2022-01-25","arxiv_id":"2201.10323","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-time-series-anomaly-detection-with","title":"Online Time Series Anomaly Detection with State Space Gaussian Processes","date":"2022-01-18","arxiv_id":"2201.06763","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecast-based-multi-aspect-framework-for","title":"Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection","date":"2022-01-13","arxiv_id":"2201.04792","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepfib-self-imputation-for-time-series-1","title":"DeepFIB: Self-Imputation for Time Series Anomaly Detection","date":"2021-12-12","arxiv_id":"2112.06247","repositories_listed":0,"syntology":null},{"url":null,"slug":"ymir-a-supervised-ensemble-framework-for","title":"Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection","date":"2021-12-09","arxiv_id":"2112.04704","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-augmented-adversarial-autoencoders-for","title":"Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction","date":"2021-10-15","arxiv_id":"2110.08306","repositories_listed":0,"syntology":null},{"url":null,"slug":"hankel-structured-tensor-robust-pca-for","title":"Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection","date":"2021-10-08","arxiv_id":"2110.04352","repositories_listed":0,"syntology":null},{"url":null,"slug":"stric-stacked-residuals-of-interpretable","title":"STRIC: Stacked Residuals of Interpretable Components for Time Series Anomaly Detection","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dae-discriminatory-auto-encoder-for","title":"DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation","date":"2021-09-08","arxiv_id":"2109.04247","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-variational-learning-for-anomaly","title":"Federated Variational Learning for Anomaly Detection in Multivariate Time Series","date":"2021-08-18","arxiv_id":"2108.08404","repositories_listed":0,"syntology":null},{"url":null,"slug":"event2graph-event-driven-bipartite-graph-for","title":"Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection","date":"2021-08-15","arxiv_id":"2108.06783","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-neuroevolution-based-approach-for","title":"Ensemble neuroevolution based approach for multivariate time series anomaly detection","date":"2021-08-08","arxiv_id":"2108.03585","repositories_listed":0,"syntology":null},{"url":null,"slug":"onlinestl-scaling-time-series-decomposition","title":"OnlineSTL: Scaling Time Series Decomposition by 100x","date":"2021-07-19","arxiv_id":"2107.09110","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-anomaly-detection-for-smart-grids","title":"Time Series Anomaly Detection for Smart Grids: A Survey","date":"2021-07-16","arxiv_id":"2107.08835","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-anomaly-detection-with-label-free","title":"Time Series Anomaly Detection with label-free Model Selection","date":"2021-06-11","arxiv_id":"2106.07473","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-in-predictive-maintenance-a","title":"Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms","date":"2021-05-26","arxiv_id":"2105.12818","repositories_listed":0,"syntology":null},{"url":null,"slug":"rlad-time-series-anomaly-detection-through","title":"RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning","date":"2021-03-31","arxiv_id":"2104.00543","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-far-should-we-look-back-to-achieve","title":"How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?","date":"2021-02-12","arxiv_id":"2102.06560","repositories_listed":0,"syntology":null},{"url":null,"slug":"nvae-gan-based-approach-for-unsupervised-time","title":"NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection","date":"2021-01-08","arxiv_id":"2101.02908","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-in-dynamical-systems-from","title":"Anomaly detection in dynamical systems from measured time series","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruct-anomaly-to-normal-adversarial","title":"Reconstruct Anomaly to Normal: Adversarial Learned and Latent Vector-constrained Autoencoder for Time-series Anomaly Detection","date":"2020-10-14","arxiv_id":"2010.06846","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-model-selection-for-time-series","title":"Automated Model Selection for Time-Series Anomaly Detection","date":"2020-08-25","arxiv_id":"2009.04395","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-robustness-on-seasonality-heavy","title":"Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection","date":"2020-07-25","arxiv_id":"2007.14254","repositories_listed":0,"syntology":null},{"url":null,"slug":"rere-a-lightweight-real-time-ready-to-go","title":"ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series","date":"2020-04-05","arxiv_id":"2004.02319","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-in-univariate-time-series-a","title":"Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art","date":"2020-04-01","arxiv_id":"2004.00433","repositories_listed":0,"syntology":null},{"url":null,"slug":"robusttad-robust-time-series-anomaly","title":"RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks","date":"2020-02-21","arxiv_id":"2002.09545","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-grammar-induction-for-detecting","title":"Ensemble Grammar Induction For Detecting Anomalies in Time Series","date":"2020-01-29","arxiv_id":"2001.11102","repositories_listed":0,"syntology":null},{"url":null,"slug":"rsm-gan-a-convolutional-recurrent-gan-for","title":"RSM-GAN: A Convolutional Recurrent GAN for Anomaly Detection in Contaminated Seasonal Multivariate Time Series","date":"2019-11-16","arxiv_id":"1911.07104","repositories_listed":0,"syntology":null},{"url":null,"slug":"warping-resilient-time-series-embeddings","title":"Warping Resilient Scalable Anomaly Detection in Time Series","date":"2019-06-12","arxiv_id":"1906.05205","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-anomaly-detection-using","title":"Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning","date":"2019-05-31","arxiv_id":"1905.13628","repositories_listed":0,"syntology":null},{"url":null,"slug":"devil-in-the-detail-attack-scenarios-in","title":"Devil in the Detail: Attack Scenarios in Industrial Applications","date":"2019-05-24","arxiv_id":"1905.10292","repositories_listed":0,"syntology":null},{"url":null,"slug":"feedforward-neural-network-for-time-series","title":"Feedforward Neural Network for Time Series Anomaly Detection","date":"2018-12-20","arxiv_id":"1812.08389","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-experienced-anomaly-detector-through","title":"Towards Experienced Anomaly Detector through Reinforcement Learning","date":"2018-04-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"greenhouse-a-zero-positive-machine-learning","title":"Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection","date":"2018-01-09","arxiv_id":"1801.03168","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-anomaly-detection-detection-of","title":"Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data","date":"2017-08-11","arxiv_id":"1708.03665","repositories_listed":0,"syntology":null}],"record_sha256":"7f23b8afee97144ee2e237d6c9331526dba5e0172173c19dda8558c3309992ce","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}