At present,the interpretation of regional economic development(RED)has changed from a simple evaluation of economic growth to a focus on economic growth and the optimization of economic structure,the improvement of ec...At present,the interpretation of regional economic development(RED)has changed from a simple evaluation of economic growth to a focus on economic growth and the optimization of economic structure,the improvement of economic relations,and the change of institutional innovation.This article uses the RED trend as the research object and constructs the RED index to conduct the theoretical analysis.Then this paper uses the attention mechanism based on digital twins and the time series network model to verify the actual data.Finally,the regional economy is predicted according to the theoretical model.The specific research work mainly includes the following aspects:1)This paper introduced the development status of research on time series networks and economic forecasting at home and abroad.2)This paper introduces the basic principles and structures of long and short-term memory(LSTM)and convolutional neural network(CNN),constructs an improved CNN-LSTM model combined with the attention mechanism,and then constructs a regional economic prediction index system.3)The best parameters of the model are selected through experiments,and the trained model is used for simulation experiment prediction.The results show that the CNN-LSTM model based on the attentionmechanism proposed in this paper has high accuracy in predicting regional economies.展开更多
Complex networks are important paradigms for analyzing the complex systems as they allow understanding the structural properties of systems composed of different interacting entities. In this work we propose a reliabl...Complex networks are important paradigms for analyzing the complex systems as they allow understanding the structural properties of systems composed of different interacting entities. In this work we propose a reliable method for constructing complex networks from chaotic time series. We first estimate the covariance matrices, then a geodesic-based distance between the covariance matrices is introduced. Consequently the network can be constructed on a Riemannian manifold where the nodes and edges correspond to the covariance matrix and geodesic-based distance, respectively. The proposed method provides us with an intrinsic geometry viewpoint to understand the time series.展开更多
Information centric networking(ICN) is a new network architecture that is centred on accessing content. It aims to solve some of the problems associated with IP networks, increasing content distribution capability and...Information centric networking(ICN) is a new network architecture that is centred on accessing content. It aims to solve some of the problems associated with IP networks, increasing content distribution capability and improving users' experience. To analyse the requests' patterns and fully utilize the universal cached contents, a novel intelligent resources management system is proposed, which enables effi cient cache resource allocation in real time, based on changing user demand patterns. The system is composed of two parts. The fi rst part is a fi ne-grain traffi c estimation algorithm called Temporal Poisson traffi c prediction(TP2) that aims at analysing the traffi c pattern(or aggregated user requests' demands) for different contents. The second part is a collaborative cache placement algorithm that is based on traffic estimated by TP2. The experimental results show that TP2 has better performance than other comparable traffi c prediction algorithms and the proposed intelligent system can increase the utilization of cache resources and improve the network capacity.展开更多
It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics （CFD） model. In order to shorten the computation time, a hybrid model...It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics （CFD） model. In order to shorten the computation time, a hybrid model based on CFD and time series neural network （TSNN） is proposed in this paper. The concentration change of radioactive contamination in an inland reservoir after a postulated accident is studied as a case. The result shows that this hybrid model can predict the contaminant diffusion trend and shorten at least 50% of iteration time. Priori knowledge integrated into the neural network model is able to reduce the mean square error of network output to 9.66×10 8 , which makes neural network output more close to the simulated contaminant concentration.展开更多
Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group stru...Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group structure to address nodal heterogeneity within the network.An iterative algorithm is employed to minimize a least-squares objective function,allowing for simultaneous estimation of both the parameters and the group structure.To determine the unknown number of groups and factors,a PIC criterion is introduced.Additionally,statistical inference of the estimated parameters is presented.To assess the validity of the proposed estimation and inference procedures,we conduct extensive numerical studies.We also demonstrate the utility of our model using a stock dataset obtained from the Chinese A-Share stock market.展开更多
Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supp...Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.展开更多
Transformer top oil temperature prediction is a research focal point in online monitoring of transformer operational status.Existing methods lack interpretability in feature selection and do not consider the temporal ...Transformer top oil temperature prediction is a research focal point in online monitoring of transformer operational status.Existing methods lack interpretability in feature selection and do not consider the temporal correlation of features.To address these issues,we provide a top oil temperature of electric transformers prediction method by using causal discovery and the Graph Neural Network(GNN)-Long Short-Term Memory(LSTM)model in this paper.To conduct feature selection,we use causal discovery to reduce feature dimensionality.Then,construct causal graph data based on the causal relationship matrix.Finally,spatiotemporal features are extracted by the GNN-LSTM model for oil temperature prediction.Experimental results demonstrate that this method can scientifically carry out feature selection,ensuring prediction accuracy and result robustness.展开更多
文摘At present,the interpretation of regional economic development(RED)has changed from a simple evaluation of economic growth to a focus on economic growth and the optimization of economic structure,the improvement of economic relations,and the change of institutional innovation.This article uses the RED trend as the research object and constructs the RED index to conduct the theoretical analysis.Then this paper uses the attention mechanism based on digital twins and the time series network model to verify the actual data.Finally,the regional economy is predicted according to the theoretical model.The specific research work mainly includes the following aspects:1)This paper introduced the development status of research on time series networks and economic forecasting at home and abroad.2)This paper introduces the basic principles and structures of long and short-term memory(LSTM)and convolutional neural network(CNN),constructs an improved CNN-LSTM model combined with the attention mechanism,and then constructs a regional economic prediction index system.3)The best parameters of the model are selected through experiments,and the trained model is used for simulation experiment prediction.The results show that the CNN-LSTM model based on the attentionmechanism proposed in this paper has high accuracy in predicting regional economies.
基金Supported by the National Natural Science Foundation of China under Grant No 61362024
文摘Complex networks are important paradigms for analyzing the complex systems as they allow understanding the structural properties of systems composed of different interacting entities. In this work we propose a reliable method for constructing complex networks from chaotic time series. We first estimate the covariance matrices, then a geodesic-based distance between the covariance matrices is introduced. Consequently the network can be constructed on a Riemannian manifold where the nodes and edges correspond to the covariance matrix and geodesic-based distance, respectively. The proposed method provides us with an intrinsic geometry viewpoint to understand the time series.
基金supported by the National High Technology Research and Development Program(863)of China(No.2015AA016101)the National Natural Science Fund(No.61300184)Beijing Nova Program(No.Z151100000315078)
文摘Information centric networking(ICN) is a new network architecture that is centred on accessing content. It aims to solve some of the problems associated with IP networks, increasing content distribution capability and improving users' experience. To analyse the requests' patterns and fully utilize the universal cached contents, a novel intelligent resources management system is proposed, which enables effi cient cache resource allocation in real time, based on changing user demand patterns. The system is composed of two parts. The fi rst part is a fi ne-grain traffi c estimation algorithm called Temporal Poisson traffi c prediction(TP2) that aims at analysing the traffi c pattern(or aggregated user requests' demands) for different contents. The second part is a collaborative cache placement algorithm that is based on traffic estimated by TP2. The experimental results show that TP2 has better performance than other comparable traffi c prediction algorithms and the proposed intelligent system can increase the utilization of cache resources and improve the network capacity.
基金Supported by the National Natural Science Foundation of China(51339004,71171151)
文摘It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics （CFD） model. In order to shorten the computation time, a hybrid model based on CFD and time series neural network （TSNN） is proposed in this paper. The concentration change of radioactive contamination in an inland reservoir after a postulated accident is studied as a case. The result shows that this hybrid model can predict the contaminant diffusion trend and shorten at least 50% of iteration time. Priori knowledge integrated into the neural network model is able to reduce the mean square error of network output to 9.66×10 8 , which makes neural network output more close to the simulated contaminant concentration.
基金Supported by National Natural Science Foundation of China(72222009,71991472)。
文摘Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group structure to address nodal heterogeneity within the network.An iterative algorithm is employed to minimize a least-squares objective function,allowing for simultaneous estimation of both the parameters and the group structure.To determine the unknown number of groups and factors,a PIC criterion is introduced.Additionally,statistical inference of the estimated parameters is presented.To assess the validity of the proposed estimation and inference procedures,we conduct extensive numerical studies.We also demonstrate the utility of our model using a stock dataset obtained from the Chinese A-Share stock market.
文摘Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.
文摘Transformer top oil temperature prediction is a research focal point in online monitoring of transformer operational status.Existing methods lack interpretability in feature selection and do not consider the temporal correlation of features.To address these issues,we provide a top oil temperature of electric transformers prediction method by using causal discovery and the Graph Neural Network(GNN)-Long Short-Term Memory(LSTM)model in this paper.To conduct feature selection,we use causal discovery to reduce feature dimensionality.Then,construct causal graph data based on the causal relationship matrix.Finally,spatiotemporal features are extracted by the GNN-LSTM model for oil temperature prediction.Experimental results demonstrate that this method can scientifically carry out feature selection,ensuring prediction accuracy and result robustness.
