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A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting
[article]
2019
arXiv
pre-print
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In preprocessing, a time series decomposition method is applied to separately feed short-term, long-term and spatial patterns into different components of a neural network. ...
In neural network architecture, each kernel of a multi-kernel convolution layer is applied to a cluster of time series to extract short-term features in neighboring areas. ...
Fully connected Long-Short Term Memory neural network (LSTM) is capable of capturing long-term temporal patterns. ...
arXiv:1902.00636v1
fatcat:5y45pzhzcvfptprelhqncbnckm
A Review of Deep Learning Research
2019
KSII Transactions on Internet and Information Systems
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This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and ...
Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms ...
The forget gate is to make the recurrent neural network "forget" information that was not used before. LSTM can more naturally remember the input long before a long time. ...
doi:10.3837/tiis.2019.04.001
fatcat:tefkvk3fvvanbkzwmjn44eoxsu
Optimizing Convolution Neural Network on the TI C6678 multicore DSP
2018
MATEC Web of Conferences
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Convolutional Neural Networks (CNNs) have become the most advanced algorithms for deep learning. They are widely used in image processing, object detection and automatic translation. ...
The efficiency of the improved convolution operation has increased by tens of times. ...
The CNN can be regarded as a special multi-layer perceptron or feedforward neural network. ...
doi:10.1051/matecconf/201824603044
fatcat:wqijomqzfzds7ashlpzlslipsa
Research on Application of Sports Training Performance Prediction Based on Convolutional Neural Network
2022
Computational and Mathematical Methods in Medicine
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Moreover, this paper calculates the regional similarity of sports training images in the fully connected layer of the convolutional neural network and introduces the local linear weighting method for analysis ...
Finally, this paper combines the convolutional neural network algorithm to construct a sports training performance prediction system to improve the effect of sports training and design experiments to verify ...
feasibility in time series. ...
doi:10.1155/2022/7295833
pmid:35371283
pmcid:PMC8975655
fatcat:5y533qsmerhgvfglwa2rtcjmq4
Parallel Sequence-Channel Projection Convolutional Neural Network for EEG-Based Emotion Recognition
2020
IEEE Access
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Therefore, complete contextual relevance can be obtained via the length-synchronized convolutional kernel. ...
Song et al. applied a dynamical graph convolutional neural network (DGCNN) to analyze EEG data [17] . Li et al. proposed a hierarchical convolutional neural network [18] . ...
doi:10.1109/access.2020.3039542
fatcat:s5lfw6uybvcw5cyymub22axa5q
Minutely Active Power Forecasting Models Using Neural Networks
2020
Sustainability
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After training and testing long short-term memory (LSTM) network variants, a convolutional neural network (CNN), and a multi-layer perceptron (MLP), we observed that the latter performed better on the ...
In this study we compared the baseline performance and structure of different types of neural networks on residential energy data by formulating a suitable supervised learning problem, based on real world ...
Prediction evaluation on 150 data points for (a) Long Short-Term Memory (LSTM) network, (b) Stacked LSTM, (c) Bidirectional LSTM, (d) 1-Dimensional Convolutional Neural Network (1D CNN), (e) Multilayer ...
doi:10.3390/su12083177
fatcat:a7rlaf436fdk3aq7tfhzt54x3a
Seizure Prediction Using Multi-View Features and Improved Convolutional Gated Recurrent Network
2020
IEEE Access
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The PLVs range from 0 to 1, where "0" means two signals are completely asynchronized, and "1" means two signals are fully synchronized. ...
For a given time series {x k }, the scale-dependent measure µ s,m0 is defined as a root-mean-square fluctuation of the integrated response time series y m around a polynomial trendŷ m,λ of order λ within ...
doi:10.1109/access.2020.3024580
fatcat:wwlyaciviverjexkjvwfylty5a
Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
[article]
2021
arXiv
pre-print
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, and (3) introducing an extra decoder based on a fully connected deep network to conquer the error propagation challenge with multi-step forecasting. ...
We propose a novel deep learning network architecture, Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Networks (AMF-STGCN), to model the traffic dynamics of mobile base stations. ...
AMF-STConv block output Layer Then, we use a two-layer fully connected neural network to generate the output of AMF-STConv block. We first reshape Ao into Ao ∈ R N ×T ×K× (Co×B) . ...
arXiv:2111.00724v1
fatcat:tr6i5ut6bbgf5awjkcc75jce3m
Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks
2021
Energy Science & Engineering
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K E Y W O R D S convolutional neural networks, deep learning, natural gas forecasting, spatio-temporal model 2 | ...
We propose a deep learning model based on spatio-temporal convolutional neural networks (DLST) to tackle the problem of gas flow forecasting in a complex high-pressure transmission network. ...
Graph based CNN are often used together with recurrent neural networks (RNN) to capture both spatial and temporal dependencies in graph structured time series data. ...
doi:10.1002/ese3.932
fatcat:xrxkgump5nhm3ds6iy7mme6z74
Multimodal Sensor Motion Intention Recognition Based on Three-Dimensional Convolutional Neural Network Algorithm
2021
Computational Intelligence and Neuroscience
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We divide the target data into multiple segments and use a three-dimensional convolutional neural network to extract the short-term spatiotemporal features. ...
The three types of features of the same segment are fused together and input into the LSTM network for time-series modeling to further fuse the features to obtain multimodal long-term spatiotemporal features ...
Convolutional neural network (CNN) is a special deep feedforward neural net- work. ...
doi:10.1155/2021/5690868
pmid:34188674
pmcid:PMC8192210
fatcat:enj3jdfaavh7vcioawkykvnafm
Research on Damage Evaluation of Radar Target Based on Deep Learning
2019
IOP Conference Series: Materials Science and Engineering
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; train the damage assessment network to make the network have strong experts Experience has led to a neural network with expert experience. ...
Determine the network parameters and network node structure characteristics according to the characteristics of damage information; use the appropriate software and tools to build a modular network model ...
This paper focuses on convolutional neural networks and restricted Boltzmann machines [1] . (1) Convolutional neural network CNN [2] [3] is an extension of neural network. ...
doi:10.1088/1757-899x/569/5/052050
fatcat:6qlwjjdvdfai7ivyzfqrjncbri
A New Hybrid Teaching Model for a Psychology Course
2021
International Journal of Emerging Technologies in Learning (iJET)
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In addition, a new performance prediction model based on graph convolution neural network is proposed decision tree data mining methods are used to predict student performance, by extracting user information ...
and high-dimensional information of the knowledge graph, this mode is conducive to platform managers to understand students' conditions in time and improve teaching quality. ...
Based on graph convolution neural network, this article proposes a feasible method to predict students' learning situation. ...
doi:10.3991/ijet.v16i03.20457
fatcat:7c2fnka335ccndrq53k3pm4zae
A Novel Multichannel Dilated Convolution Neural Network for Human Activity Recognition
2020
Mathematical Problems in Engineering
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A novel multichannel dilated convolution neural network for improving the accuracy of human activity recognition is proposed. ...
and not to consider the use of the pooling layers that are used in the traditional convolution with dilated convolution. ...
Based on current research deficiencies, this paper proposes a novel multichannel dilated convolution neural network (MDCNN). e model can get a larger receptive field to extract global features of long-time ...
doi:10.1155/2020/5426532
fatcat:ekfnqzgvszgytj34ftarq3c2om
Early-Stage Gas Identification Using Convolutional Long Short-Term Neural Network with Sensor Array Time Series Data
2021
Sensors
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In this context, a tailored approach so-called convolutional long short-term memory (CLSTM) neural network is developed to perform the identification task incorporating temporal characteristics within ...
subsequent gas identification using time-series data during the transitional phase before reaching equilibrium. ...
In this context, we develop a convolutional long short-term memory (CLSTM) neural network with the convolutional layers embedded to rapidly and automatically extract the low-level features from the time-series ...
doi:10.3390/s21144826
fatcat:e7mlx5667zfchaz4m6n2c5mipy
STCGAT: Spatial-temporal causal networks for complex urban road traffic flow prediction
[article]
2022
arXiv
pre-print
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Existing approaches usually use fixed traffic road network topology maps and independent time series modules to capture Spatial-temporal correlations, ignoring the dynamic changes of traffic road networks ...
The model dynamically captures the spatial dependence of the traffic network through a Graph Attention Network(GAT) and then analyzes the causal relationship of the traffic data using our proposed Causal ...
convolutional network is mainly composed of a combination of Bi-directional Long-short Term Memory(BiLSTM) [11] and TCN; 3) Prediction layer: Prediction results are output using a fully connected neural ...
arXiv:2203.10749v1
fatcat:k5aw6iqhofdu7bep35fymp3f4m
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