Unsupervised online detection and prediction of outliers in streams of sensor data

Abstract

Outliers are unexpected observations, which deviate from the majority of observations. Outlier detection and prediction are challenging tasks, because outliers are rare by definition. A stream is an unbounded source of data, which has to be processed promptly. This article proposes novel methods for outlier detection and outlier prediction in streams of sensor data. The outlier detection is an independent, unsupervised process, which is implemented using an autoencoder. The outlier detection continuously evaluates if the latest data point x i from a stream is an inlier or an outlier. This distinction is based on the reconstruction cost accompanied with Chebyshev's inequality and the EWMA (exponentially weighted moving average) model. The outlier prediction uses the results of the outlier detection to form the required training data. The outlier prediction utilizes LR (logistic regression), SGD (stochastic gradient descent) and the hidden representation provided by the autoencoder to predict outliers in streams. The results of the experiments show that the proposed methods (1) provide accurate results, (2) are calculated in reduced computation time and (3) use a low amount of memory. Our proposed methods are suitable for analyzing streams of sensor data and providing results with low latency. The experiments also indicated that the outlier prediction is able to anticipate the occurrence of outliers in streams of sensor data.

Deep Quantile Regression for Unsupervised Anomaly Detection in Time-Series

“…This section covers the extensive experiments conducted to test and compare the performance of DQR-AD with six other state-of-the-art anomaly detection methods that model prediction error using Gaussian distribution to identify anomalies. These methods include DeepAnT [33], NumentaTM [52], ContextOSE [58], EXPoSE [59], AE [38], and VAE-LSTM [39]. The experiment is conducted using real and synthetic datasets from different application domain.…”

“…This approach is computationally demanding or impose strong requirements. Reunanen et al [38] proposed another unsupervised anomaly detection method that combine autoencoder and logistic regression for outlier detection and prediction in sensor data streams. The autoencoder reconstructs the input data and produces hidden representation of the input that can be used to create the required labels for logistic regression to classify anomalous points.…”

“…In addition, for each timeseries point, an anomaly score is calculated which helps in handling the type of anomaly detection problem. Deep learning-based anomaly detection techniques using long short-term memory (LSTM) [28][29][30] and other forms of recurrent neural network (RNN) [31,32], convolutional neural network (CNN) [33], and autoencoder [15,[34][35][36][37][38][39] demonstrate higher performance over the previously mentioned prediction-base anomaly detection techniques. Despite their performance and unsupervised learning approach, they compute the anomaly score by assuming a distribution usually of Gaussian type on the prediction error, which is either ranked, or threshold to label data instances as anomalous or not.…”

Dynamic graph embedding for outlier detection on multiple meteorological time series

“…The proposed model was applied on three real-world datasets, and it achieved better results than baselines. However, most time series are not labeled; therefore, many unsupervised learning methods have been proposed to address this issue [ 17 , 18 ]. Autoencoder is an unsupervised learning model that has been widely applied to outlier detection on time series [ 19 , 20 ].…”

Towards Outlier Sensor Detection in Ambient Intelligent Platforms—A Low-Complexity Statistical Approach

“…Reunanen et al [ 13 ] use LR (Logistic Regression) to predict outliers in streams of sensor data. The approach predicts the occurrence of outliers in t time steps in the future; however, it has to use labeled data—normal and anomalous observations—previously identified with an auto-encoder algorithm.…”