An Anomaly Detection Algorithm Selection Service for IoT Stream Data Based on Tsfresh Tool and Genetic Algorithm
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Zhongguo Yang | Sikandar Ali | Mingzhu Zhang | Irshad Ahmed Abbasi | Elfatih Elmubarak Mustafa | Mingzhu Zhang | Zhongguo Yang | Sikandar Ali | I. A. Abbasi
[1] Fei Tony Liu,et al. Isolation-Based Anomaly Detection , 2012, TKDD.
[2] Shenglin Zhang,et al. Rapid Deployment of Anomaly Detection Models for Large Number of Emerging KPI Streams , 2018, 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC).
[3] Chao Yi,et al. Time-Series Anomaly Detection Service at Microsoft , 2019, KDD.
[4] Andreas W. Kempa-Liehr,et al. Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh - A Python package) , 2018, Neurocomputing.
[5] Nenad Stojanovic,et al. Big-data-driven anomaly detection in industry (4.0): An approach and a case study , 2016, 2016 IEEE International Conference on Big Data (Big Data).
[6] Lovekesh Vig,et al. LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection , 2016, ArXiv.
[7] Sebastian Wagner,et al. Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art , 2020, ArXiv.
[8] Yanbo Han,et al. A Data-Driven Service Creation Approach for Effectively Capturing Events from Multiple Sensor Streams , 2019, 2019 IEEE International Conference on Web Services (ICWS).
[9] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.
[10] Subutai Ahmad,et al. Evaluating Real-Time Anomaly Detection Algorithms -- The Numenta Anomaly Benchmark , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).
[11] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[12] Qiming Chen,et al. Experience in Continuous analytics as a Service (CaaaS) , 2011, EDBT/ICDT '11.
[13] Saeed Amizadeh,et al. Generic and Scalable Framework for Automated Time-series Anomaly Detection , 2015, KDD.
[14] Chen Liu,et al. A Service Selection Framework for Anomaly Detection in IoT Stream Data , 2020, 2020 International Conference on Service Science (ICSS).
[15] Vipin Kumar,et al. Comparative Evaluation of Anomaly Detection Techniques for Sequence Data , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[16] J. Ma,et al. Time-series novelty detection using one-class support vector machines , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[17] Jehn-Ruey Jiang,et al. Anomaly Detection for Univariate Time Series with Statistics and Deep Learning , 2019, 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE).
[18] Ali Kashif Bashir,et al. Data mining and machine learning methods for sustainable smart cities traffic classification: A survey , 2020, Sustainable Cities and Society.
[19] Takehisa Yairi,et al. Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction , 2014, MLSDA'14.