A hybrid time series model based on AR-EMD and volatility for medical data forecasting: A case study in the emergency department

[1]  Zhiwang Zhang,et al.  Two-phase multi-kernel LP-SVR for feature sparsification and forecasting , 2016, Neurocomputing.

[2]  Shari J. Welch,et al.  Forecasting daily patient volumes in the emergency department. , 2008, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[3]  Shyi-Ming Chen,et al.  Forecasting enrollments based on fuzzy time series , 1996, Fuzzy Sets Syst..

[4]  Hui-Kuang Yu Weighted fuzzy time series models for TAIEX forecasting , 2005 .

[5]  Mieko Tanaka-Yamawaki,et al.  Adaptive use of technical indicators for the prediction of intra-day stock prices , 2007 .

[6]  Weilin Li,et al.  Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings , 2017 .

[7]  Chao Liu,et al.  Short-term prediction of wind power using EMD and chaotic theory , 2012 .

[8]  Sung Wan Hwang,et al.  Development of a revisit prediction model for the outpatient in a hospital , 2008 .

[9]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[10]  Ching-Chiang Yeh,et al.  A Hybrid Model by Empirical Mode Decomposition and Support Vector Regression for Tourist Arrivals Forecasting , 2013 .

[11]  B. Chissom,et al.  Forecasting enrollments with fuzzy time series—part II , 1993 .

[12]  Ping-Feng Pai,et al.  Predicting engine reliability by support vector machines , 2006 .

[13]  Ling Tang,et al.  Electricity price forecasting with a BED (Bivariate EMD Denoising) methodology , 2015 .

[14]  Tae Hyup Roh Forecasting the volatility of stock price index , 2007, Expert Syst. Appl..

[15]  C. Jothi Venkateswaran,et al.  Reverse Sequential Covering Algorithm for Medical Data Mining , 2015 .

[16]  Ching-Hsue Cheng,et al.  A novel time-series model based on empirical mode decomposition for forecasting TAIEX , 2014 .

[17]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[18]  Liang-Ying Wei,et al.  A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting , 2016, Appl. Soft Comput..

[19]  Prashant C. Palvia,et al.  Critical information technology issues in Turkish healthcare , 2014, Inf. Manag..

[20]  Liu Yuan,et al.  Study on network traffic forecast model of SVR optimized by GAFSA , 2013 .

[21]  Wang Jun,et al.  A weighted EMD-based prediction model based on TOPSIS and feed forward neural network for noised time series , 2017, Knowl. Based Syst..

[22]  Ingoo Han,et al.  Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index , 2000 .

[23]  M. Friedman The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[24]  E. Ionides,et al.  Forecasting models of emergency department crowding. , 2009, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[25]  T. Ouarda,et al.  Generalized autoregressive conditional heteroscedasticity modelling of hydrologic time series , 2012 .

[26]  B. Gordon,et al.  Developing models for patient flow and daily surge capacity research. , 2006, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[27]  Liang-Ying Wei,et al.  A GA-weighted ANFIS model based on multiple stock market volatility causality for TAIEX forecasting , 2013, Appl. Soft Comput..

[28]  Chuanrui Fu,et al.  Forecasting Exchange Rate with EMD-Based Support Vector Regression , 2010, 2010 International Conference on Management and Service Science.

[29]  Astrid Guttmann,et al.  Emergency Department Flow Measures for Adult and Pediatric Patients in British Columbia and Ontario: A Retrospective, Repeated Cross-Sectional Study. , 2017, The Journal of emergency medicine.

[30]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[31]  Jiang Hao,et al.  GA-EMD-SVR condition prediction for a certain diesel engine , 2010, 2010 Prognostics and System Health Management Conference.

[32]  Kun Guo,et al.  Forecasting China's Service Outsourcing Development with an EMD-VAR-SVR Ensemble Method☆ , 2016 .

[33]  Ahmet Arslan,et al.  Different medical data mining approaches based prediction of ischemic stroke , 2016, Comput. Methods Programs Biomed..

[34]  Jianqiang Li,et al.  Emerging information technologies for enhanced healthcare , 2015, Comput. Ind..

[35]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[36]  C. W. Tong,et al.  A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation , 2015 .

[37]  Ingoo Han,et al.  An evolutionary approach to the combination of multiple classifiers to predict a stock price index , 2006, Expert Syst. Appl..

[38]  Dejie Yu,et al.  Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings , 2005 .

[39]  Bao Rong Chang,et al.  Resolving the forecasting problems of overshoot and volatility clustering using ANFIS coupling nonlinear heteroscedasticity with quantum tuning , 2008, Fuzzy Sets Syst..

[40]  Ching-Chiang Yeh,et al.  Forecasting the output of Taiwan's integrated circuit (IC) industry using empirical mode decomposition and support vector machines , 2012 .

[41]  Kunhuang Huarng,et al.  Effective lengths of intervals to improve forecasting in fuzzy time series , 2001, Fuzzy Sets Syst..

[42]  Ponnuthurai N. Suganthan,et al.  A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting , 2016, IEEE Transactions on Neural Networks and Learning Systems.