STAD-FEBTE, a shallow and supervised framework for time series anomaly detection by automatic feature engineering, balancing, and tree-based ensembles: An industrial case study
暂无分享,去创建一个
[1] Dandan Peng,et al. A Multihead ConvLSTM for Time Series Classification in eHealth Industry 4.0 , 2022, Wireless Communications and Mobile Computing.
[2] Junchi Yan,et al. Transformers in Time Series: A Survey , 2022, IJCAI.
[3] Kevin I-Kai Wang,et al. Expect the Unexpected: Unsupervised Feature Selection for Automated Sensor Anomaly Detection , 2021, IEEE Sensors Journal.
[4] Alexandros Iosifidis,et al. Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving , 2021, Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems.
[5] Xiuzhen Cheng,et al. Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT , 2021, IEEE Internet of Things Journal.
[6] Thomas G. Dietterich,et al. A Unifying Review of Deep and Shallow Anomaly Detection , 2020, Proceedings of the IEEE.
[7] S. Cronin,et al. Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand , 2020, Nature Communications.
[8] Lu Liu,et al. Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline , 2020, ArXiv.
[9] Jose A. Lozano,et al. A Review on Outlier/Anomaly Detection in Time Series Data , 2020, ACM Comput. Surv..
[10] Akbar Siami Namin,et al. The Performance of LSTM and BiLSTM in Forecasting Time Series , 2019, 2019 IEEE International Conference on Big Data (Big Data).
[11] T. Besier,et al. Feature engineering workflow for activity recognition from synchronized inertial measurement units , 2019, ACPR Workshops.
[12] Enrique Onieva,et al. Multi-head CNN-RNN for multi-time series anomaly detection: An industrial case study , 2019, Neurocomputing.
[13] Nick S. Jones,et al. catch22: CAnonical Time-series CHaracteristics , 2019, Data Mining and Knowledge Discovery.
[14] Andreas W. Kempa-Liehr,et al. Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh - A Python package) , 2018, Neurocomputing.
[15] Jinhai Liu,et al. Markov chain-based feature extraction for anomaly detection in time series and its industrial application , 2018, 2018 Chinese Control And Decision Conference (CCDC).
[16] Wenfeng Li,et al. Optimizing multi-sensor deployment via ensemble pruning for wearable activity recognition , 2018, Inf. Fusion.
[17] Sungzoon Cho,et al. Squeezed Convolutional Variational AutoEncoder for unsupervised anomaly detection in edge device industrial Internet of Things , 2017, 2018 International Conference on Information and Computer Technologies (ICICT).
[18] Tie-Yan Liu,et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.
[19] Dong Yue,et al. Hierarchical Time Series Feature Extraction for Power Consumption Anomaly Detection , 2017, LSMS/ICSEE.
[20] Anna Veronika Dorogush,et al. CatBoost: unbiased boosting with categorical features , 2017, NeurIPS.
[21] John Cristian Borges Gamboa,et al. Deep Learning for Time-Series Analysis , 2017, ArXiv.
[22] Andreas W. Kempa-Liehr,et al. Distributed and parallel time series feature extraction for industrial big data applications , 2016, ArXiv.
[23] Fernando Nogueira,et al. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning , 2016, J. Mach. Learn. Res..
[24] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[25] Jean Paul Barddal,et al. A Survey on Feature Drift Adaptation , 2015, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI).
[26] Samuel H. Huang. Supervised feature selection: A tutorial , 2015, Artif. Intell. Res..
[27] Max A. Little,et al. Highly comparative time-series analysis: the empirical structure of time series and their methods , 2013, Journal of The Royal Society Interface.
[28] Gilles Louppe,et al. Ensembles on Random Patches , 2012, ECML/PKDD.
[29] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[30] Pierre Geurts,et al. Extremely randomized trees , 2006, Machine Learning.
[31] Gustavo E. A. P. A. Batista,et al. A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.
[32] Gustavo E. A. P. A. Batista,et al. Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior , 2004, MICAI.
[33] L. Breiman. Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.
[34] Kaspar Althoefer,et al. Theoretical modelling of the self-tapping screw fastening process , 2001 .
[35] Ibrahim A. Hameed,et al. A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives , 2021 .
[36] Ravid Shwartz-Ziv,et al. Tabular Data: Deep Learning is Not All You Need , 2021 .
[37] THE COMPARISON OF THE KNOWN MODELS OF SELF-TAPPING SCREW JOINTS , 2017 .
[38] B. Priya,et al. A Review of Dimensionality Reduction Techniques , 2015 .
[39] Trevor Hastie,et al. Multi-class AdaBoost ∗ , 2009 .
[40] Lior Rokach,et al. Ensemble learning: A survey , 2018, WIREs Data Mining Knowl. Discov..
[41] Nam H. Nguyen,et al. Submitted to Ieee Transactions on Signal Processing 1 Collaborative Multi-sensor Classification via Sparsity-based Representation , 2022 .