scholarly journals Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval

2020 ◽  
Vol 34 (02) ◽  
pp. 1403-1411
Author(s):  
Dixian Zhu ◽  
Dongjin Song ◽  
Yuncong Chen ◽  
Cristian Lumezanu ◽  
Wei Cheng ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Multivariate Time Series ◽  
Series Data ◽  
Generative Adversarial Networks ◽  
Current Time ◽  
Binary Coding ◽  
Effective Manner

Multivariate time series data are becoming increasingly ubiquitous in varies real-world applications such as smart city, power plant monitoring, wearable devices, etc. Given the current time series segment, how to retrieve similar segments within the historical data in an efficient and effective manner is becoming increasingly important. As it can facilitate underlying applications such as system status identification, anomaly detection, etc. Despite the fact that various binary coding techniques can be applied to this task, few of them are specially designed for multivariate time series data in an unsupervised setting. To this end, we present Deep Unsupervised Binary Coding Networks (DUBCNs) to perform multivariate time series retrieval. DUBCNs employ the Long Short-Term Memory (LSTM) encoder-decoder framework to capture the temporal dynamics within the input segment and consist of three key components, i.e., a temporal encoding mechanism to capture the temporal order of different segments within a mini-batch, a clustering loss on the hidden feature space to capture the hidden feature structure, and an adversarial loss based upon Generative Adversarial Networks (GANs) to enhance the generalization capability of the generated binary codes. Thoroughly empirical studies on three public datasets demonstrated that the proposed DUBCNs can outperform state-of-the-art unsupervised binary coding techniques.

Remaining Useful Life Estimation and Prognostication of SAC305 PCB's for Dynamic Conditions of Temperature and Vibration Loads

2021 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Ken Blecker
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Remaining Useful Life ◽  
Series Data ◽  
Sac305 Solder ◽  
Feature Vectors ◽  
Useful Life

Abstract This study focuses on the feature vector identification and Remaining Useful Life (RUL) estimation of SAC305 solder alloy PCB's of two different configurations during varying conditions of temperature and vibration. The feature vectors are identified using the strain signals acquired from four symmetrical locations of the PCB at regular intervals during vibration. Two different types of experiments are employed to characterize the PCB's dynamic changes with varying temperature and acceleration levels. The strain signals acquired during each of these experiments are compared based on both time and frequency domain characteristics. Different statistical and frequency-based techniques were used to identify the strain signal variations with changes in the environment and loading conditions. The feature vectors in predicting failure at a constant working temperature and load were identified, and as an extension to this work, the effectiveness of the feature vectors during varying conditions of temperature and acceleration levels are investigated. The remaining Useful Life of the packages was estimated using a deep learning approach based on Long Short Term Memory (LSTM) network. This technique can identify the underlying patterns in multivariate time series data that can predict the packages' life. The autocorrelation function's residuals were used as the multivariate time series data in conjunction with the LSTM deep learning technique to forecast the packages' life at different varying temperatures and acceleration levels during vibration.

scholarly journals Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 876 ◽  
Author(s):  
Renzhuo Wan ◽  
Shuping Mei ◽  
Jun Wang ◽  
Min Liu ◽  
Fan Yang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Multivariate Time Series ◽  
Series Data ◽  
Asymmetric Structure ◽  
Convolutional Network

Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods are proposed. To improve the prediction accuracy and minimize the multivariate time series data dependence for aperiodic data, in this article, Beijing PM2.5 and ISO-NE Dataset are analyzed by a novel Multivariate Temporal Convolution Network (M-TCN) model. In this model, multi-variable time series prediction is constructed as a sequence-to-sequence scenario for non-periodic datasets. The multichannel residual blocks in parallel with asymmetric structure based on deep convolution neural network is proposed. The results are compared with rich competitive algorithms of long short term memory (LSTM), convolutional LSTM (ConvLSTM), Temporal Convolution Network (TCN) and Multivariate Attention LSTM-FCN (MALSTM-FCN), which indicate significant improvement of prediction accuracy, robust and generalization of our model.

scholarly journals Unsupervised Pre-training of a Deep LSTM-based Stacked Autoencoder for Multivariate Time Series Forecasting Problems

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Alaa Sagheer ◽  
Mostafa Kotb
Keyword(s):  
Time Series ◽  
Case Studies ◽  
Real World ◽  
Time Series Data ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Series Data ◽  
Recurrent Networks ◽  
Stacked Autoencoder ◽  
Real World Datasets

AbstractCurrently, most real-world time series datasets are multivariate and are rich in dynamical information of the underlying system. Such datasets are attracting much attention; therefore, the need for accurate modelling of such high-dimensional datasets is increasing. Recently, the deep architecture of the recurrent neural network (RNN) and its variant long short-term memory (LSTM) have been proven to be more accurate than traditional statistical methods in modelling time series data. Despite the reported advantages of the deep LSTM model, its performance in modelling multivariate time series (MTS) data has not been satisfactory, particularly when attempting to process highly non-linear and long-interval MTS datasets. The reason is that the supervised learning approach initializes the neurons randomly in such recurrent networks, disabling the neurons that ultimately must properly learn the latent features of the correlated variables included in the MTS dataset. In this paper, we propose a pre-trained LSTM-based stacked autoencoder (LSTM-SAE) approach in an unsupervised learning fashion to replace the random weight initialization strategy adopted in deep LSTM recurrent networks. For evaluation purposes, two different case studies that include real-world datasets are investigated, where the performance of the proposed approach compares favourably with the deep LSTM approach. In addition, the proposed approach outperforms several reference models investigating the same case studies. Overall, the experimental results clearly show that the unsupervised pre-training approach improves the performance of deep LSTM and leads to better and faster convergence than other models.

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

scholarly journals Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1633
Author(s):  
Elena-Simona Apostol ◽  
Ciprian-Octavian Truică ◽  
Florin Pop ◽  
Christian Esposito
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Change Point ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Change Point Detection ◽  
Change Points ◽  
Series Data ◽  
Prediction And Forecasting ◽  
Point Detection

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

scholarly journals Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

2021 ◽  
Vol 13 (3) ◽  
pp. 67
Author(s):  
Eric Hitimana ◽  
Gaurav Bajpai ◽  
Richard Musabe ◽  
Louis Sibomana ◽  
Jayavel Kayalvizhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Human Beings ◽  
Feed Forward Network

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

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