Detecting Anomalies in Time Series Data via a Meta-Feature Based Approach

Hu, Min; Z, Ji; Yan, Ke; Guo, Yanan; Feng, Xia‐Ting; Gong, Jiaheng; Zhao, Xin; Dong, Ligang

doi:10.1109/access.2018.2840086

Cited by 71 publications

(49 citation statements)

References 68 publications

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“…This technique is also used for detecting anomalies in activities of daily life for example sleeping, sitting, and walking patterns [19]. Another time series anomaly detection technique based on OCSVM was proposed by Hu et al [20]. In this technique, six meta-features on actual univariate or multivariant time series are defined first and then OCSVM is applied on meta-featurebased data space to find abnormal states.…”

Section: Literature Review Of Anomaly Detection Methodsmentioning

confidence: 99%

DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

et al. 2019

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Traditional distance and density-based anomaly detection techniques are unable to detect periodic and seasonality related point anomalies which occur commonly in streaming data, leaving a big gap in time series anomaly detection in the current era of the IoT. To address this problem, we present a novel deep learning-based anomaly detection approach (DeepAnT) for time series data, which is equally applicable to the non-streaming cases. DeepAnT is capable of detecting a wide range of anomalies, i.e., point anomalies, contextual anomalies, and discords in time series data. In contrast to the anomaly detection methods where anomalies are learned, DeepAnT uses unlabeled data to capture and learn the data distribution that is used to forecast the normal behavior of a time series. DeepAnT consists of two modules: time series predictor and anomaly detector. The time series predictor module uses deep convolutional neural network (CNN) to predict the next time stamp on the defined horizon. This module takes a window of time series (used as a context) and attempts to predict the next time stamp. The predicted value is then passed to the anomaly detector module, which is responsible for tagging the corresponding time stamp as normal or abnormal. DeepAnT can be trained even without removing the anomalies from the given data set. Generally, in deep learningbased approaches, a lot of data are required to train a model. Whereas in DeepAnT, a model can be trained on relatively small data set while achieving good generalization capabilities due to the effective parameter sharing of the CNN. As the anomaly detection in DeepAnT is unsupervised, it does not rely on anomaly labels at the time of model generation. Therefore, this approach can be directly applied to real-life scenarios where it is practically impossible to label a big stream of data coming from heterogeneous sensors comprising of both normal as well as anomalous points. We have performed a detailed evaluation of 15 algorithms on 10 anomaly detection benchmarks, which contain a total of 433 real and synthetic time series. Experiments show that DeepAnT outperforms the state-of-the-art anomaly detection methods in most of the cases, while performing on par with others. INDEX TERMS Anomaly detection, artificial intelligence, convolutional neural network, deep neural networks, recurrent neural networks, time series analysis.

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Section: Literature Review Of Anomaly Detection Methodsmentioning

confidence: 99%

DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

et al. 2019

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“…Moreover, univariate data forecasting remains as one of the most challenging problems in the field of machine learning, since most of the dependent variables are unknown, such as the electric current, voltage, weather conditions, etc. [7]. Classic univariate forecasting methods are usually applied to cases that either the rest of the features are too difficult to be measured or there are too many variables to be measured, e.g., the stock market indices forecasting problems [8].…”

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confidence: 99%

Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy

et al. 2018

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Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage.

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“…The data-driven methods mainly refer to ML methods. Recent study shows that ML-based forecasting strategies usually provide extremely high prediction results for time series data forecasting problems [3,16]. Ocak and Seker [17] compared artificial neural network (ANN), support vector machine (SVM), and Gaussian processes (GPs) on surface settlement forecasting calculation caused by earth pressure balance machines (EPBMs).…”

Section: Related Workmentioning

confidence: 99%

Tunnel Surface Settlement Forecasting with Ensemble Learning

Yan

Dai

et al. 2019

Sustainability

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Ground surface settlement forecasting in the process of tunnel construction is one of the most important techniques towards sustainable city development and preventing serious damages, such as landscape collapse. It is evident that modern artificial intelligence (AI) models, such as artificial neural network, extreme learning machine, and support vector regression, are capable of providing reliable forecasting results for tunnel surface settlement. However, two limitations exist for the current forecasting techniques. First, the data provided by the construction company are usually univariate (i.e., containing only the settlement data). Second, the demand of tunnel surface settlement is immediate after the construction process begins. The number of training data samples is limited. Targeting at the above two limitations, in this study, a novel ensemble machine learning model is proposed to forecast tunnel surface settlement using univariate short period of real-world tunnel settlement data. The proposed Adaboost.RT framework fully utilizes existing data points with three base machine learning models and iteratively updates hyperparameters using current surface point locations. Experimental results show that compared with existing machine learning techniques and algorithms, the proposed ensemble learning method provides a higher prediction accuracy with acceptable computational efficiency.

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Detecting Anomalies in Time Series Data via a Meta-Feature Based Approach

Cited by 71 publications

References 68 publications

DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy

Tunnel Surface Settlement Forecasting with Ensemble Learning

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