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moving average model with an Alternative GARCH-Type Error

Journal of Systems Science and Information 2018年第2期9卷 165-177页

作者： Huafeng ZHU Xingfa ZHANG Xin LIANG Yuan LI School of Economics and Statistics Guangzhou University School of Mathematics and Statistics Guangxi Normal University

Motivated by the double autoregressive model with order p(DAR(p) model), in this paper,we study the moving average model with an alternative GARCH error. The model is an extension from DAR(p) model by letting the order p goes to infinity. The quasi maximum likelihood estimator of the parameters in the model is shown to be asymptotically normal, without any strong moment *** results confirm that our estimators perform well. We also apply our model to study a real data set and it has better fitting performance compared to DAR model for the considered data.

关键词： moving average model double autoregressive model quasi maximum likelihood estimator

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Fusing moving average model and stationary wavelet decomposition for automatic incident detection:case study of Tokyo Expressway

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Journal of Traffic and Transportation Engineering(English Edition) 2014年第6期1卷 404-414页

作者： Qinghua Liu Edward Chung Liujia Zhai School of Computer Science and Engineering Jiangsu University of Science and Technology School of Transportation Engineering Tongji University Smart Transport Research Centre Queensland University of TechnologyBrisbaneQueenslandAustralia

Traffic congestion is a growing problem in urban areas all over the world. The transport sector has been in full swing event study on intelligent transportation system for automatic detection. The functionality of automatic incident detection on expressways is a primary objective of advanced traffic management system. In order to save lives and prevent secondary incidents, accurate and prompt incident detection is necessary. This paper presents a methodology that integrates moving average （MA） model with stationary wavelet decomposition for automatic incident detection, in which parameters of layer coefficient are extracted from the difference between the upstream and downstream occupancy. Unlike other wavelet-based method presented before, firstly it smooths the raw data with MA model. Then it uses stationary wavelet to decompose, which can achieve accurate reconstruction of the signal, and does not shift the signal transfer coefficients. Thus, it can detect the incidents more accurately. The threshold to trigger incident alarm is also adjusted according to normal traffic condition with con- gestion. The methodology is validated with real data from Tokyo Expressway ultrasonic sensors. Ex- perimental results show that it is accurate and effective, and that it can differentiate traffic accident from other condition such as recurring traffic congestion.

关键词： automatic incident detection moving average model stationary wavelet decomposition Tokyo Expressway

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Exponential Bounds for Ruin Probability in Two moving average Risk models with Constant Interest Rate

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Acta Mathematica Sinica,English Series 2008年第2期24卷 319-328页

作者： Ding Jun YAO Rong Ming WANG Department of Statistics East China Normal University Shanghai 200062 P. R. China

The authors consider two discrete-time insurance risk models. Two moving average risk models are introduced to model the surplus process, and the probabilities of ruin are examined in models with a constant interest f... 详细信息

关键词： ruin probability moving average model rate of interest exponential bound martingale

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Predicting the output error of the suboptimal state estimator to improve the performance of the MPC-based artificial pancreas

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Control Theory and Technology 2023年第4期21卷 541-554页

作者： Martin Dodek Eva Miklovičová Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information TechnologySlovak University of Technology in BratislavaIlkovičova 3Bratislava81219Slovakia

The error of single step-ahead output prediction is the information traditionally used to correct the state estimate while exploiting the new measurement of the system ***,its dynamics and statistical properties can be further studied and exploited in other *** is known that in the case of suboptimal state estimation,this output prediction error forms a correlated sequence,hence it can be effectively predicted in real *** a suboptimal scenario is typical in applications where the process noise model is not known or it is ***,the paper deals with the problems of analytical and empirical modeling,identification,and prediction of the output error of the suboptimal state estimator for the sake of improving the output prediction accuracy and ultimately the performance of the model predictive *** improvements are validated on an empirical model of type 1 diabetes within an in-silico experiment focused on glycemia prediction and implementation of the MPC-based artificial pancreas.

关键词： Suboptimal state estimation Prediction Autoregressive model moving average model model predictive control Diabetes mellitus Artificial pancreas

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Hybrid dynamic prediction model of bus arrival time based on weighted of historical and real-time GPS Data

Hybrid dynamic prediction model of bus arrival time based on...

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第25届中国控制与决策会议

作者： Jun Gong Mingyue Liu Sen Zhang Department of Systems Engineering Key Lab of Integrated Automation of Process Industry of MOE Northeastern University

ISBN: (纸本)9781467355339

Advanced traveler information systems （ATIS） are one component of intelligent transportation systems （ITS）, and a major component of ATIS is travel time information. Global positioning system-based automatic vehicle location （AVL） systems have been adopted by many transit agencies for tracking their vehicles and predicting travel time in real time. It is a very important subject to improve the precision and reliability of the prediction model which can attract additional ridership, reduce passengers’ anxieties and waiting times at bus stop, and increase their satisfaction. Furthermore, it can promote the development of city public transportation. This paper presents an improved approach to predict the public bus arrival time based on historical and real-time GPS data. After analyzing the components of bus arrival time systematically, the bus arrival time and dwell time at previous stops are chosen as the main input variables of the prediction model. At first, the algorithm of data interpolation and processing is designed to get the real-time GPS data as the input variables of the prediction models. Secondly, the statistical model is obtained based on the historical data of average running time of each link and dwelling time of each stop at given time-of-day and day-of-week, respectively. Thirdly, a hybrid dynamic prediction model is proposed to predict the bus arrival time. Finally, Actual GPS data from bus route 244 located in Shenyang, CHINA are used as a test bed. The index of Mean Absolute Percentage Error （MAPE） is used to evaluate the three models. The results show that the improved model outperforms the historical data based model in terms of prediction accuracy.

关键词： bus arrival time intelligent transportation system moving average model

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