A Time Convolutional Network Based Outlier Detection for Multidimensional Time Series in Cyber-Physical-Social Systems

Abstract

With the development of the Cyber-Physical-Social Systems(CPSS), a large number of multidimensional time series have been generated in today's world, such as: sensor data for industrial equipment operation, vehicle driving data, and cloud server operation and maintenance data and so on. The key task of Cloud-Fog-Edge Computing in managing these systems is how to detect anomalous data in a specific time series to facilitate operator action to solve potential system problems. So multidimensional time series outlier detection become an important direction of CPSS data mining and Cloud-Fog-Edge Computing research, and it has a wide range of applications in industry, finance, medicine and other fields. This paper proposes a framework called Multidimensional time series Outlier detection based on a Time Convolutional Network AutoEncoder (MOTCN-AE), which can detect outliers in time series data, such as identifying equipment failures, dangerous driving behaviors of cars, etc. Specifically, this paper first uses a feature extraction method to transform the original time series into a feature-rich time series. Second, the proposed TCN-AE is used to reconstruct the feature-rich time series data, and the reconstruction error is used to calculate outlier scores. Finally, the MOTCN-AE framework is validated by multiple time series datasets to demonstrate its effectiveness in detecting time series outliers. INDEX TERMS Time series, outlier detection, time convolution network, autoencoder.

Deep Learning Based Anomaly Detection for Muti-dimensional Time Series: A Survey

“…Kieu et al [56] divided the time series into multiple sliding windows, extracted eight-dimensional features for each time window, and spliced them with external semantic information, and the reconstruction error is trained to the minimum by inputting to LSTM-AE and CNN-AE. Meng et al [57] expanded on the work of [56] and combined time convolutional networks with autoencoders to detect abnormal points in the time series of the Cyber Physical Social System (CPSS). Zhang et al [58] proposed a multi-angle convolutional recursive autoencoder (MSCRED), which first calculates the feature matrix for the multi-dimensional features of each moment in the time dimension, and then uses CNN-AE and ConvLSTM to learn the spatial semantics of the time series.…”

Unsupervised Deep Learning for IoT Time Series

“…Generative Model-Based Feature Extraction Autoencoder [15] (Bi-direction dilated recurrent autoencoder), [94] (Convolutional autoencoder), [95] (Bi-direction recurrent autoencoder), [96] (Convolutional autoencoder), [109] (Bi-direction DAE), [110] (SAE), [111] (SAE), [112] (VAE), [113] (Convolutional autoencoder), [114] (Convolutional autoencoder), [115] (Convolutional autoencoder), [116] (Convolutional autoencoder and recurrent autoencoder), [117] (Time convolutional autoencoder) GAN [118] (Recurrent GAN), [119] (GAN)…”

“…Meng et al [117] captured features of Cyber-Physical-Social Systems (CPSS) time series with a time convolutional network-based automatic encoder (TCN-AE). It's composed of causal convolution, dilated convolution, a residual module, and an FCN.…”

Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis

“…Additionally, the authors use the LSTM on a univariate power demand data set to validate their findings. Meng et al [ 25 ] use a time convolutional AE (TCN-AE) to classify outliers. Savic et al [ 26 ] train two competing AEs, trained offline, stripped to run on edge devices, to classify abnormal behavior in of CPSs in cellular networks.…”