A spatio-temporal decomposition based deep neural network for time series forecasting

Asadi, Reza; Regan, Amelia

doi:10.1016/j.asoc.2019.105963

Cited by 77 publications

(33 citation statements)

References 56 publications

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“…ANN is the most common and widely used algorithm in science and engineering [54][55][56][57][58] which has been of great interest due to the multilayered network having the capability to extract features [59,60]. The dataset enters through the input layer, passes by the hidden layer for the extraction of the useful features.…”

Section: B Artificial Neural Networkmentioning

confidence: 99%

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

Akbar

Sung

2021

IEEE Access

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Device simulation has been explored and industrialized for over 40 years; however, it still requires huge computational cost. Therefore, it can be further advanced using deep learning (DL) algorithms. We for the first time report an efficient and accurate DL approach with device simulation for gate-all-around silicon nanowire metal-oxide-semiconductor field-effect transistors (MOSFETs) to predict electrical characteristics of device induced by work function fluctuation. By using three different DL models: artificial neural network (ANN), convolutional neural network (CNN), and long short term memory (LSTM), the variability of threshold voltage, on-current and off-current is predicted with respect to different metal-grain number and location of the low and high values of work function. The comparison is established among the ANN, CNN and the LSTM models and results depict that the CNN model outperforms in terms of the root mean squared error and the percentage error rate. The integration of device simulation with DL models exhibits the characteristic estimation of the explored device efficiently; and, the accurate prediction from the DL models can accelerate the process of device simulation. Notably, the DL approach is able to extract crucial electrical characteristics of a complicated device accurately with 2% error in a cost-effective manner computationally.

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Section: B Artificial Neural Networkmentioning

confidence: 99%

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

Akbar

Sung

2021

IEEE Access

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“…To address this concern, we change the batch size in a limited range such that it will not affect the convergence. Careful design of the model's architecture and dynamic selection of hyper parameters such as batch size and learning rate results in better convergence rate and higher achieved accuracy [4]. Dynamic selection of learning rate will be added to the decision-making function in the future work.…”

Section: Monitoring and Decision Makingmentioning

confidence: 99%

HyperTune

HeydariGorji

Rezaei

Torabzadehkashi

et al. 2020

Proceedings of the 39th International Conference on Computer-Aided Design

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“…It also has been found that both historical time data and the road network spatial relationship have an impact on traffic flow, and such a finding led to the joint deep learning model combining spatial-temporal advantages being applied to traffic flow prediction [24]. Asadi and Regan [25] proposed a CNN-LSTM deep learning framework for spatiotemporal forecasting problems. LSTM (Long Short Term Memory) is a variant of RNN [26], which solves the problem of gradient explosion and disappearance of RNN by adding memory modules, and has achieved positive performance in time series prediction [27,28].…”

Section: Introductionmentioning

confidence: 99%

A Multifeatures Spatial-Temporal-Based Neural Network Model for Truck Flow Prediction

Wang

Shao

Zhai³

et al. 2021

Journal of Advanced Transportation

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The majority of studies on road traffic flow prediction have focused on the flow of passenger cars or the flow of traffic as a whole, which ignore the significant impact of trucks with different sizes and operational characteristics on traffic flow efficiency. Therefore, in this paper, we focus on truck traffic flow and propose a Multifeatures Spatial-Temporal-Based Neural Network model (M-BiCNNGRU) to improve its prediction. The proposed model not only comprises conventional temporal characteristics and spatial relationships but also includes a range of multifeatures. These multifeatures include policy limit, optimal time delay, road resistance, and traffic congestion state. The impacts of upstream and downstream road sections are considered on the spatial relationship by using a Convolutional Neural Network (CNN). A Bidirectional Gated Recurrent Unit (Bi-GRU) is employed to account for the temporal characteristics. To evaluate the proposed model, traffic flow data were collected from a major expressway in Beijing and the results were compared with those derived from existing models. The results show that the prediction accuracy of the BiCNNGRU model, with spatial-temporal characteristics, and the M-BiGRU model, with multifeatures and temporal, is, respectively, 4.13% and 2.15% greater than that of the Bi-GRU model, with temporal characteristics. The prediction accuracy of the proposed M-BiCNNGRU model is 92.86%, which is 7.12% greater than that of the Bi-GRU model and 13.83% greater than that of the Support Vector Regression (SVR) model. In general, therefore, the proposed M-BiCNNGRU model, which combines multifeatures, temporal characteristics, and spatial relationships, can significantly improve accuracy in predicting truck traffic flow.

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A spatio-temporal decomposition based deep neural network for time series forecasting

Cited by 77 publications

References 56 publications

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

HyperTune

A Multifeatures Spatial-Temporal-Based Neural Network Model for Truck Flow Prediction

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