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Methods In Neuronal Modeling From Ions To Networks

The primary objective of this study is to predict network-wide multi-step-ahead flight delay. A novel graph-to-sequence learning architecture with attention mechanism (AG2S-Net) is developed to predict the multi-step-ahead hourly departure and arrival delay of the entire network. The proposed AG2S-Net consists of a graph convolution neural network, a bi-LSTM neural network, and a sequence-to-sequence framework with embedded attention mechanism. Five-year flight data of 75 airports and 242 links are collected from the National Airspace System to illustrate the procedure. First, K-means clustering algorithm is applied to classify the hourly flight delay states of the entire network into four typical delay patterns, which are further used as explanatory variables in prediction model. Then, the network-wide multi-step-ahead flight delay prediction model is built with the proposed AG2S-Net. The result indicates that the model achieves better performance on large-scale airports than small-scale airports. The delay pattern variables and weather variables can greatly improve the prediction performance. In addition, compared with several benchmark methods, the AG2S-Net performs the best for all three airport types in terms of the lowest RMSE and MAE values. Finally, the graph network analysis further reveals that the proposed AG2S-Net can capture the hidden correlations between airports without links and collect information from more airports in the same community to enhance the prediction performance. The results of this study could provide insightful suggestions for aviation authorities and airport regulators to develop effective air traffic control strategies for alleviating flight delays and improving operation efficiency of the entire network.

Graph to sequence learning with attention mechanism for network-wide multi-step-ahead flight delay prediction

Traditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network’s topologies. Over a decade, various researchers have been working with these issues; however, it remains a challenge. Therefore, this review paper presents a comprehensive review of significant research conducted on various approaches for chaotic time series forecasting, using machine learning techniques such as convolutional neural network (CNN), wavelet neural network (WNN), fuzzy neural network (FNN), and long short-term memory (LSTM) in the nonlinear systems aforementioned above. The paper also aims to provide issues of individual forecasting approaches for better understanding and up-to-date knowledge for chaotic time series forecasting. The comprehensive review table summarizes the works closely associated with the mentioned issues. It includes published year, research country, forecasting approach, application, forecasting parameters, performance measures, and collected data area in this sector. Future improvements and current studies in this field are broadly examined. In addition, possible future scopes and limitations are closely discussed.

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Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Measures of spike train synchrony for data with multiple time scales

Abstract The importance of robust flight delay prediction has recently increased in the air transportation industry. This industry seeks alternative methods and technologies for more robust flight delay prediction because of its significance for all stakeholders. The most affected are airlines that suffer from monetary and passenger loyalty losses. Several studies have attempted to analysed and solve flight delay prediction problems using machine learning methods. This research proposes a novel alternative method, namely social ski driver conditional autoregressive-based (SSDCA-based) deep learning. Our proposed method combines the Social Ski Driver algorithm with Conditional Autoregressive Value at Risk by Regression Quantiles. We consider the most relevant instances from the training dataset, which are the delayed flights. We applied data transformation to stabilise the data variance using Yeo-Johnson. We then perform the training and testing of our data using deep recurrent neural network (DRNN) and SSDCA-based algorithms. The SSDCA-based optimisation algorithm helped us choose the right network architecture with better accuracy and less error than the existing literature. The results of our proposed SSDCA-based method and existing benchmark methods were compared. The efficiency and computational time of our proposed method are compared against the existing benchmark methods. The SSDCA-based DRNN provides a more accurate flight delay prediction with 0.9361 and 0.9252 accuracy rates on both dataset-1 and dataset-2, respectively. To show the reliability of our method, we compared it with other meta-heuristic approaches. The result is that the SSDCA-based DRNN outperformed all existing benchmark methods tested in our experiment. 

/pdf/social-ski-driver-conditional-autoregressive-based-deep-1poh7cf1.pdf

Social ski driver conditional autoregressive-based deep learning classifier for flight delay prediction

Nowadays, the civil aviation industry has a high precision demand of flight delay prediction. To make full use of the characteristics of flight data and meteorological data, two flight delay prediction models using deep convolution neural network based on fusion of meteorological data are proposed in this paper. One is DCNN (Dual-channel Convolutional Neural Network), which refers to the ResNet network structure. The other is SE-DenseNet (Squeeze and Excitation-Densely Connected Convolutional Network), combining the advantages of DenseNet and SENet. Firstly, flight data and meteorological data are fused in the model. Then, both DCNN and SE-DenseNet models are used to extract feature automatically based on the fused flight data set. Finally, the softmax classifier is adopted to predict the flight delay level. For proposed DCNN model, both straight channel and convolution channel are designed to guarantee the lossless transmission of the feature matrix and enhance the patency of the deep network. For proposed SE-DenseNet model, a SE module is added after the convolution layer of each DenseNet block, which can not only enhance the transmission of deep information but also achieve feature recalibration in the feature extraction process. The research results indicate that after considering characteristics of meteorological information, the accuracy of the model can be improved 1% compared with only considering the flight information. The two deep convolutional neural networks proposed in this paper, DCNN and SE-DenseNet, can both effectively improve the prediction accuracies, reaching to 92.1% and 93.19%, respectively.

Flight Delay Prediction Using Deep Convolutional Neural Network Based on Fusion of Meteorological Data

In this paper, the dynamics of a map-based model is proposed firstly. It is a simple model which can not only reproduce rich behaviors of biological neurons but also compute efficiently. Then, the dynamics of two coupled maps that model the behavior of two electrically coupled neurons is discussed. By tuning the coupling strength, synchronization of two spiking or bursting neurons are simulated. Furthermore, the spatiotemporal behavior of a small-world neuronal network using map-based model is studied. Detailed investigations reveal that the collective dynamics of neuronal activity will be affected by varying some key parameters, such as the coupling strength of neurons, connection probability and the number of nearest neighbor in small-world topology. Our study suggests that the map-based model will give us a new opportunity to reproduce the real biological network containing a large number of neurons.

Spatiotemporal Behavior of Small-World Neuronal Networks Using a Map-Based Model

In both experimental and computational neuroscience, it is a frequent task to estimate the degree of synchrony or reliability between two or multiple spike trains. ISI-distance method is one of the most common used methods based on the coefficient of variation, which is parameter free and time scale independent. However, it can only measure regular spiking synchronization well while not good at measuring bursting synchronization. In this paper, an improved method of measuring multiple spike train synchrony is proposed, which can measure both regular and burst spike trains well. Using a simulated neuronal network of Hindmarsh–Rose neurons, it is demonstrated that the performance of our methods in distinguish different levels of multiple neuron spike train synchrony performs as well as previous methods. We expect that the method proposed in this paper will be an overall tool for measuring the synchrony of all kinds of spike trains and applications.

An Improved Method of Measuring Multiple Spike Train Synchrony

In the olfactory system, both the temporal spike structure and spatial distribution of neuronal activity are important for processing odor information. In this paper, a biophysically-detailed, spiking neuronal model is used to simulate the activity of olfactory bulb. It is shown that by varying some key parameters such as maximal conductances of Ks, Nap and Na, the spike train of single neuron can exhibit various firing patterns. In the olfactory bulb, synchronization in coupled neurons is also investigated as the coupling strength gets increased. Synchronization process can be identified by correlation coefficient and phase plot. It is illustrated that the coupled neurons can exhibit different types of synchronization when the coupling strength increases. These results may be instructive to understand information transmission in olfactory system.

Spike train pattern and firing synchronization in a model of the olfactory mitral cell

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Papers

Flight Delay Prediction Using Deep Convolutional Neural Network Based on Fusion of Meteorological Data

Spatiotemporal Behavior of Small-World Neuronal Networks Using a Map-Based Model

An Improved Method of Measuring Multiple Spike Train Synchrony

Spike train pattern and firing synchronization in a model of the olfactory mitral cell