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Combining Deep Neural Networks and Classical Time Series Regression Models for Forecasting Patient Flows in Hong Kong

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收录情况: ◇ SCIE ◇ EI

机构: [1]School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China [2]Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, China [3]School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China [4]College of Materials Science and Engineering, Shenzhen University, Shenzhen 518060, China [5]Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu 610039, China [6]Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan [7]Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong [8]Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100054, China
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关键词: Data mining deep neural networks hybrid approach time series regression data mining

摘要:
Deep Neural Networks (DNNs) has been dominating recent data mining related tasks with better performances. This article proposes a hybrid approach that exploits the unique predictive power of DNN and classical time series regression models, including Generalized Linear Model (GLM), Seasonal AutoRegressive Integrated Moving Average model (SARIMA) and AutoRegressive Integrated Moving Average with eXplanatory variable (ARIMAX) method, in forecasting time series in reality. For each selected time series regression model, three different hybrid strategies are designed in order to merge its results with DNNs, namely, Zhang's method, Khashei's method, and moving average filter-based method. The real seasonal time series data of patient arrival volume in a Hong Kong A&ED center was collected for the period July 1, 2009, through June 30, 2011 and is used for comparing the forecast accuracy of proposed hybrid strategies. The mean absolute percentage error (MAPE) is set as the metric and the result indicates that all hybrid models achieved higher accuracy than original single models. Among 3 hybrid strategies, generally, Khashei's method and moving average filter-based method achieve lower MAPE than Zhang's method. Furthermore, the predicted value is an important prerequisite of conducting the rostering and scheduling in A&ED center, either in the simulation-based approach or in the mathematical programming approach.

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出版当年[2018]版:
大类 | 2 区 工程技术
小类 | 2 区 计算机:信息系统 3 区 工程:电子与电气 3 区 电信学
最新[2023]版:
大类 | 3 区 计算机科学
小类 | 3 区 工程:电子与电气 4 区 计算机:信息系统 4 区 电信学
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出版当年[2017]版:
Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Q1 TELECOMMUNICATIONS Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
最新[2023]版:
Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Q2 TELECOMMUNICATIONS

影响因子: 最新[2023版] 最新五年平均 出版当年[2017版] 出版当年五年平均 出版前一年[2016版] 出版后一年[2018版]

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第一作者机构: [1]School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China [2]Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, China [3]School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China
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通讯机构: [1]School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China [2]Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, China
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