Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detec...Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detection schemes are unsupervised methods,such as anomaly detection methods based on density,distance and clustering.In total,unsupervised anomaly detection methods have many limitations.For example,they cannot be well combined with prior knowledge in some anomaly detection tasks.For some nonlinear anomaly detection tasks,the modeling is complex and faces dimensional disasters,which are greatly affected by noise.Sometimes it is difficult to find abnormal events that users are interested in,and users need to customize model parameters before detection.With the wide application of deep learning technology,it has a good modeling ability to solve linear and nonlinear data relationships,but the application of deep learning technology in the field of anomaly detection has many challenges.If we regard exceptions as a supervised problem,exceptions are a few,and we usually face the problem of too few labels.To obtain a model that performs well in the anomaly detection task,it requires a high initial training set.Therefore,to solve the above problems,this paper proposes a supervised learning method with manual participation.We introduce the integrated learning model and train a supervised anomaly detection model with strong stability and high accuracy through active learning technology.In addition,this paper adopts certain strategies to maximize the accuracy of anomaly detection and minimize the cost of manual labeling.In the experimental link,we will show that our method is better than some traditional anomaly detection algorithms.展开更多
Most of the current methods for anomaly detection in time series are unsupervised.However,unsupervised learning assumes the distribution of the data and cannot obtain satisfactory results in some scenarios.In this pap...Most of the current methods for anomaly detection in time series are unsupervised.However,unsupervised learning assumes the distribution of the data and cannot obtain satisfactory results in some scenarios.In this paper,we design a semisupervised time series anomaly detection algorithm based on metric learning.The algorithm model mines the features in the time series from the perspectives of the time domain and frequency domain.Furthermore,we design a loss function for anomaly detection.Different from the two-class loss function,in the scenario of the loss function we designed,the normal data will be clustered and distributed in the embedding space,and the abnormal data will be far from the normal data distribution.Furthermore,we extend our designed metric learning model to a semisupervised learning model,extending the labeled dataset with the unlabeled dataset by setting different confidence levels.We conduct experiments on different public datasets and compare them with commonly used time series anomaly detection algorithms.The results show that our model has a good effect.At the same time the semisupervised setting does improve the accuracy of model detection.展开更多
基金supported by the State Grid Research Project“Study on Intelligent Analysis Technology of Abnormal Power Data Quality based on Rule Mining” (5700-202119176A-0-0-00).
文摘Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detection schemes are unsupervised methods,such as anomaly detection methods based on density,distance and clustering.In total,unsupervised anomaly detection methods have many limitations.For example,they cannot be well combined with prior knowledge in some anomaly detection tasks.For some nonlinear anomaly detection tasks,the modeling is complex and faces dimensional disasters,which are greatly affected by noise.Sometimes it is difficult to find abnormal events that users are interested in,and users need to customize model parameters before detection.With the wide application of deep learning technology,it has a good modeling ability to solve linear and nonlinear data relationships,but the application of deep learning technology in the field of anomaly detection has many challenges.If we regard exceptions as a supervised problem,exceptions are a few,and we usually face the problem of too few labels.To obtain a model that performs well in the anomaly detection task,it requires a high initial training set.Therefore,to solve the above problems,this paper proposes a supervised learning method with manual participation.We introduce the integrated learning model and train a supervised anomaly detection model with strong stability and high accuracy through active learning technology.In addition,this paper adopts certain strategies to maximize the accuracy of anomaly detection and minimize the cost of manual labeling.In the experimental link,we will show that our method is better than some traditional anomaly detection algorithms.
基金supported by State Grid Research Project“Study on Intelligent Analysis Technology of Abnormal Power Data Quality based on Rule Mining” (5700-202119176A-0-0-00).
文摘Most of the current methods for anomaly detection in time series are unsupervised.However,unsupervised learning assumes the distribution of the data and cannot obtain satisfactory results in some scenarios.In this paper,we design a semisupervised time series anomaly detection algorithm based on metric learning.The algorithm model mines the features in the time series from the perspectives of the time domain and frequency domain.Furthermore,we design a loss function for anomaly detection.Different from the two-class loss function,in the scenario of the loss function we designed,the normal data will be clustered and distributed in the embedding space,and the abnormal data will be far from the normal data distribution.Furthermore,we extend our designed metric learning model to a semisupervised learning model,extending the labeled dataset with the unlabeled dataset by setting different confidence levels.We conduct experiments on different public datasets and compare them with commonly used time series anomaly detection algorithms.The results show that our model has a good effect.At the same time the semisupervised setting does improve the accuracy of model detection.
