[1] |
Cheng C H, Wang J W, Li C H. Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix[J]. Expert Systems with Applications, 2008, 34(4): 2568-2575.
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[2] |
Hadavandia E, Shavandi H, Ghanbaric A, et al. Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals[J]. Applied Soft Computing, 2012, 12(2): 700-711.
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[3] |
Garg B, Beg M S, Ansari A. A new computational fuzzy time series model to forecast number of outpatient visits[C]// 2012 Annual Meeting of the North American. Berkeley, USA: Fuzzy Information Processing Society, 2012: 1-6.
|
[4] |
李婧, 陈瑛瑛, 霍永胜,等. 新疆某三级综合医院门诊量预测模型构建及应用[J]. 中国医院统计, 2015, 22(3): 183-185, 189.Li J, Chen Y Y, Huo Y S, et al. Construction and application of an ARIMA model for predicting the number of outpatient visits in a Xinjiang tertiary general hospital[J]. Chinese Journal of Hospital Statistics, 2015, 22(3): 183-185, 189.
|
[5] |
周忠彬, 吕红梅, 邹郢. ARIMA干预模型在医院门诊量预测中的应用[J]. 中国医院统计, 2008, 15(2): 110-112.Zhou Z B, Lu H M, Zou Y. Time series analysis by ARIMA interfering model to forecast amount of outpatient[J]. Chinese Journal of Hospital Statistics, 2008, 15(2): 110-112.
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[6] |
Box G E P, Jenkins G M. Reinsel G C. Time Series Analysis: Forecasting and Control[M]. 3ed Englewood Cliffs, USA: Prentice-Hall,1994.
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[7] |
Yu L, Lai K K, Wang S Y. Multistage RBF neural network ensemble learning for exchange rates forecasting[J]. Neurocomputing, 2008, 71(16-18): 3295-3302.
|
[8] |
Niu D X, Liu D, Wu D S. A soft computing system for day-ahead electricity price forecasting. Applied Soft Computing, 2010, 10(3): 868-875.
|
[9] |
Chang J R, Wei L Y, Cheng C H. A hybrid ANFIS model based on AR and volatility for TAIEX forecasting[J]. Applied Soft Computing, 2011, 11(1): 1388-1395.
|
[10] |
Xiao Y, Xiao J, Liu J, et al. A multiscale modeling approach incorporating ARIMA and ANNs for financial market volatility forecasting[J]. Journal of Systems Science and Complexity, 2014, 27(1): 225-236.
|
[11] |
Rumelhart D, Hinton G, Williams R. Learning representations by back-propagating errors[J]. Nature, 1986, 323: 533-536.
|
[12] |
Zhang G P. Time series forecasting using a hybrid ARIMA and neural network model[J]. Neurocomputing, 2003, 50(1): 159-175.
|
[13] |
Huang W, Lai K, Nakamori Y, et al. Neural networks in finance and economics forecasting[J].International Journal of Information Technology & Decision Making, 2007, 6(1): 113-140.
|
[14] |
Hornik K, Stinnchcombe M, White H. Multilayer feed forward networks are universal approximators[J]. Neural Networks, 1989, 2(5): 359-366.
|
[15] |
Hippert H S, Pedreira C E, Souza R C. Neural networks for short-term load forecasting: A review and evaluation[J]. IEEE Transactions on Power Systems, 2001, 16(1): 44-55.
|
[16] |
Xie J X, Cheng C T, Chau K W, et al. A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity[J]. International Journal of Environment and Pollution, 2006, 28(3/4): 364-381.
|
[17] |
Yu L, Wang S Y, Lai K K. A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates[J]. Computers & Operations Research, 2005, 32(10): 2523-2541.
|
[18] |
Zhang G Q, Patuwo B E, Hu M Y. Forecasting with artificial neural networks: The state of the art[J]. International Journal of Forecasting, 1998, 14(1): 35-62.
|
[19] |
Dickey D A, Fuller W A. Distribution of the estimators for autoregressive time series with a unit root[J]. Journal of the American Statistical Association, 1979, 74(366): 427-431.
|
[20] |
Phillips P C B, Perron P. Testing for a unit rootin time series regression[J]. Biomètrika, 1986, 75(2): 335-346. )
|
[1] |
Cheng C H, Wang J W, Li C H. Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix[J]. Expert Systems with Applications, 2008, 34(4): 2568-2575.
|
[2] |
Hadavandia E, Shavandi H, Ghanbaric A, et al. Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals[J]. Applied Soft Computing, 2012, 12(2): 700-711.
|
[3] |
Garg B, Beg M S, Ansari A. A new computational fuzzy time series model to forecast number of outpatient visits[C]// 2012 Annual Meeting of the North American. Berkeley, USA: Fuzzy Information Processing Society, 2012: 1-6.
|
[4] |
李婧, 陈瑛瑛, 霍永胜,等. 新疆某三级综合医院门诊量预测模型构建及应用[J]. 中国医院统计, 2015, 22(3): 183-185, 189.Li J, Chen Y Y, Huo Y S, et al. Construction and application of an ARIMA model for predicting the number of outpatient visits in a Xinjiang tertiary general hospital[J]. Chinese Journal of Hospital Statistics, 2015, 22(3): 183-185, 189.
|
[5] |
周忠彬, 吕红梅, 邹郢. ARIMA干预模型在医院门诊量预测中的应用[J]. 中国医院统计, 2008, 15(2): 110-112.Zhou Z B, Lu H M, Zou Y. Time series analysis by ARIMA interfering model to forecast amount of outpatient[J]. Chinese Journal of Hospital Statistics, 2008, 15(2): 110-112.
|
[6] |
Box G E P, Jenkins G M. Reinsel G C. Time Series Analysis: Forecasting and Control[M]. 3ed Englewood Cliffs, USA: Prentice-Hall,1994.
|
[7] |
Yu L, Lai K K, Wang S Y. Multistage RBF neural network ensemble learning for exchange rates forecasting[J]. Neurocomputing, 2008, 71(16-18): 3295-3302.
|
[8] |
Niu D X, Liu D, Wu D S. A soft computing system for day-ahead electricity price forecasting. Applied Soft Computing, 2010, 10(3): 868-875.
|
[9] |
Chang J R, Wei L Y, Cheng C H. A hybrid ANFIS model based on AR and volatility for TAIEX forecasting[J]. Applied Soft Computing, 2011, 11(1): 1388-1395.
|
[10] |
Xiao Y, Xiao J, Liu J, et al. A multiscale modeling approach incorporating ARIMA and ANNs for financial market volatility forecasting[J]. Journal of Systems Science and Complexity, 2014, 27(1): 225-236.
|
[11] |
Rumelhart D, Hinton G, Williams R. Learning representations by back-propagating errors[J]. Nature, 1986, 323: 533-536.
|
[12] |
Zhang G P. Time series forecasting using a hybrid ARIMA and neural network model[J]. Neurocomputing, 2003, 50(1): 159-175.
|
[13] |
Huang W, Lai K, Nakamori Y, et al. Neural networks in finance and economics forecasting[J].International Journal of Information Technology & Decision Making, 2007, 6(1): 113-140.
|
[14] |
Hornik K, Stinnchcombe M, White H. Multilayer feed forward networks are universal approximators[J]. Neural Networks, 1989, 2(5): 359-366.
|
[15] |
Hippert H S, Pedreira C E, Souza R C. Neural networks for short-term load forecasting: A review and evaluation[J]. IEEE Transactions on Power Systems, 2001, 16(1): 44-55.
|
[16] |
Xie J X, Cheng C T, Chau K W, et al. A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity[J]. International Journal of Environment and Pollution, 2006, 28(3/4): 364-381.
|
[17] |
Yu L, Wang S Y, Lai K K. A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates[J]. Computers & Operations Research, 2005, 32(10): 2523-2541.
|
[18] |
Zhang G Q, Patuwo B E, Hu M Y. Forecasting with artificial neural networks: The state of the art[J]. International Journal of Forecasting, 1998, 14(1): 35-62.
|
[19] |
Dickey D A, Fuller W A. Distribution of the estimators for autoregressive time series with a unit root[J]. Journal of the American Statistical Association, 1979, 74(366): 427-431.
|
[20] |
Phillips P C B, Perron P. Testing for a unit rootin time series regression[J]. Biomètrika, 1986, 75(2): 335-346. )
|