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

Wan, Renzhuo; Mei, Shuping; Wang, Jun; Liu, Min; Yang, Fan

doi:10.3390/electronics8080876

Cited by 205 publications

(82 citation statements)

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“…In the experiments, two benchmark datasets including a Beijing PM2.5 dataset and an ISO-NE Dataset were used to compare the M-TCN with other methods for evaluating the proposed method. The results show that the root mean squared errors (RMSEs) of each case by the M-TCN were lower than the RMSEs of each case with other methods (i.e., long short term memory (LSTM), convolutional LSTM (ConvLSTM), Temporal Convolution Network (TCN) and Multivariate Attention LSTM-FCN (MALSTM-FCN)) [44].…”

Section: Wan Et Al From China In "Multivariate Temporal Convolutionamentioning

confidence: 90%

“…) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were given in these articles, and the results indicated that the performance of the improved deep learning methods could be higher than the performance of conventional machine learning methods [43][44][45][46][47][48][49][50][51][52][53][54][55][56].…”

Section: Discussionmentioning

confidence: 99%

“…Four papers on environmental information analyses and predictions are as follows: (1) "A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence" by Panapakidis et al [43]; (2) "Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting" by Wan et al [44]; (3) "Modeling and Analysis of Adaptive Temperature Compensation for Humidity Sensors" by Xu et al [45]; (4) "An Image Compression Method for Video Surveillance System in Underground Mines Based on Residual Networks and Discrete Wavelet Transform" by Zhang et al [46].…”

Section: Environmental Information Analyses and Predictionsmentioning

confidence: 99%

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Deep Learning Applications with Practical Measured Results in Electronics Industries

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This editorial introduces the Special Issue, entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries”, of Electronics. Topics covered in this issue include four main parts: (I) environmental information analyses and predictions, (II) unmanned aerial vehicle (UAV) and object tracking applications, (III) measurement and denoising techniques, and (IV) recommendation systems and education systems. Four papers on environmental information analyses and predictions are as follows: (1) “A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence” by Panapakidis et al.; (2) “Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting” by Wan et al.; (3) “Modeling and Analysis of Adaptive Temperature Compensation for Humidity Sensors” by Xu et al.; (4) “An Image Compression Method for Video Surveillance System in Underground Mines Based on Residual Networks and Discrete Wavelet Transform” by Zhang et al. Three papers on UAV and object tracking applications are as follows: (1) “Trajectory Planning Algorithm of UAV Based on System Positioning Accuracy Constraints” by Zhou et al.; (2) “OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance” by Zhang et al.; (3) “Model Update Strategies about Object Tracking: A State of the Art Review” by Wang et al. Five papers on measurement and denoising techniques are as follows: (1) “Characterization and Correction of the Geometric Errors in Using Confocal Microscope for Extended Topography Measurement. Part I: Models, Algorithms Development and Validation” by Wang et al.; (2) “Characterization and Correction of the Geometric Errors Using a Confocal Microscope for Extended Topography Measurement, Part II: Experimental Study and Uncertainty Evaluation” by Wang et al.; (3) “Deep Transfer HSI Classification Method Based on Information Measure and Optimal Neighborhood Noise Reduction” by Lin et al.; (4) “Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets” by Chang et al.; (5) “High-Resolution Image Inpainting Based on Multi-Scale Neural Network” by Sun et al. Two papers on recommendation systems and education systems are as follows: (1) “Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing” by Sulikowski et al. and (2) “Generative Adversarial Network Based Neural Audio Caption Model for Oral Evaluation” by Zhang et al.

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Section: Wan Et Al From China In "Multivariate Temporal Convolutionamentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Environmental Information Analyses and Predictionsmentioning

confidence: 99%

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Deep Learning Applications with Practical Measured Results in Electronics Industries

et al. 2020

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“…The second is a trainable fully-connected, which performs classification based on the features learned in the previous stage. We develop our CNN architecture based on a feature extractor which comprises a convolution layer, an activation layer, a pooling layer and a fully connected layer, each of which requires a feature map as input and as output [31], as described in Figure 2.…”

Section: Cnnmentioning

confidence: 99%

Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models

Zerkouk

Chikhaoui

2020

Sensors

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The ability to identify and accurately predict abnormal behavior is important for health monitoring systems in smart environments. Specifically, for elderly persons wishing to maintain their independence and comfort in their living spaces, abnormal behaviors observed during activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we investigate a variety of deep learning models such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM and Autoencoder-CNN-LSTM for identifying and accurately predicting the abnormal behaviors of elderly people. The temporal information and spatial sequences collected over time are used to generate models, which can be fitted to the training data and the fitted model can be used to make a prediction. We present an experimental evaluation of these models performance in identifying and predicting elderly persons abnormal behaviors in smart homes, via extensive testing on two public data sets, taking into account different models architectures and tuning the hyperparameters for each model. The performance evaluation is focused on accuracy measure.However, dependent persons are often exposed to problems of various types that may cause them to perform activities of daily living incorrectly. Detecting abnormal behaviors is thus of great importance for dependent people, in order to ensure that activities are performed correctly without errors [5]. This will also ensure their safety and well-being.Detecting an anomaly in a person's activities of daily living (ADL) is usually done by detecting nonconformities with their usual ADL patterns. Various authors have used classical machine learning algorithms to achieve this [6,7]. Tele-health care requires systems with high accuracy, low computational time and minimal user intervention because data are becoming larger and more complex [8]. Deep learning architectures provide a way to automatically extract useful and meaningful spatial and temporal features from raw data without the need for data labeling, which is time consuming, complex and error prone [9,10]. This makes deep learning models easily generalizable to different contexts. LSTM is a powerful deep learning model for sequence prediction and anomaly detection in sequential data [11]. The LSTM model is able to extract temporal features with long-term relationships. CNN has powerful spatial feature extraction capabilities and the ability to detect abnormality, which is considered as a classification problem. The advantage in the use of CNN with LSTM is to combine their capabilities in terms of spatial and temporal features extraction. Autoencoder learns a compressed input representation, which is recommended for the high dimensional data collected from smart home. Therefore, the motivation to select our deep learning models is due to their high performance accuracy in terms of abnormal behavior detection in different research areas [12,13].This propert...

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“…Furthermore, they present other advantages over RNNs such as lower memory requirements, parallel processing of long sequences as opposed to the sequential approach of RNNs, and a more stable training scheme. Several works have already used successfully TCNs for time series forecasting tasks: the original architecture using stacked dilated convolutions was proposed in [25] to improve the performance of LSTM networks for financial domain problems; [26] designed a deep TCN for multiple related time series with an encoder-decoder scheme, evaluating over data from the sales domain; the study in [27] proposes a multivariate time series forecasting model for meteorological data, which outperforms several popular deep learning models. However, to the best of our knowledge, the potential of TCNs has not yet been explored for univariate time series forecasting problems related to electricity demand data.In this work, we study the applicability and performance of TCNs for multi-step time series forecasting over two energy-related datasets.…”

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confidence: 99%

Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting

Lara-Benítez¹,

Carranza-García²,

Luna-Romera³

et al. 2020

Preprint

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Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these type of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand, and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

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

Cited by 205 publications

References 28 publications

Deep Learning Applications with Practical Measured Results in Electronics Industries

Deep Learning Applications with Practical Measured Results in Electronics Industries

Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models

Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting

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