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区间时间序列的混合预测模型

, PP. 1915-1920

Keywords: 区间分析,时间序列,混合模型,ARIMA,模型,人工神经网络,城市轨道交通,Monte,Carlo方法

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Abstract:

提出一种基于自回归求和移动平均(ARIMA)与人工神经网络(ANN)的区间时间序列混合模型,并用混合模型分别对区间中值序列和区间半径序列建模.采用MonteCarlo方法生成模拟区间序列,分别用ARIMA、ANN和混合模型3种方法进行建模和预测实验,并用统计学方法检验模型误差.最后分别采用3种方法对H市轨道交通某号线牵引能耗区间序列进行了建模和预测,实验结果表明混合模型的建模精度和预测性能均优于单一模型.

References

[1]  Moore R E. Interval arithmetic and automatic error analysis in digital computing[M]. Starford: Stanford University Press, 1962: 1-35.
[2]  Box G E P, Jenkins G M, Reinsel G C. Time series analysis: Forecasting and control[M]. 4th ed. New York: JohnWiley & Sons, 2008: 21-224.
[3]  Engle R F. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom Inflation[J]. Econometrica, 1982, 50(4): 987-1007.
[4]  Roque A M, Mate C, Arroyo J, et al. iMLP: Applying multi-layer perceptrons to interval-valued data[J]. Neural Processing Letters, 2007, 25(2): 157-169.
[5]  曹玉苹, 田学民. 基于SVM和Kalman 预测的非线性系统故障预报[J]. 控制与决策, 2009, 24(3): 477-480.
[6]  (Cao Y P, Tian X M. Nonlinear system fault prognosis based on SVM and Kalman predictor[J]. Control and Decision, 2009, 24(3): 477-480.)
[7]  Arroyo J, Maté C. Introducing interval time series: Accuracy measures[C]. Proc in Computational Statistics. Heidelberg: Springer, 2006: 1139-1146.
[8]  Zemouri R, Gouriveau R, Zerhouni N. Defining and applying prediction performance metrics on a recurrent NARX time series model[J]. Neurocomputing, 2010, 73(13/14/15): 2506-2521.
[9]  Ma Q L, Zheng Q L, Peng H, et al. Multi-step-prediction of chaotic time series based on co-evolutionary recurrent neural network[J]. Chinese Physics B, 2008, 17(2): 536-542.
[10]  李松, 刘力军, 解永乐. 遗传算法优化BP 神经网络的短时交通流混沌预测[J]. 控制与决策, 2011, 26(10): 1581-1585.
[11]  (Li S, Liu L J, Xie Y L. Chaotic prediction for short-term traffic flow of optimized BP neural network based on genetic algorithm[J]. Control and Decision, 2011, 26(10): 1581-1585.)
[12]  Zhang G. Time series forecasting using a hybrid ARIMA and neural network model[J]. Neurocomputing, 2003, 50(1/2/3): 159-175.
[13]  Araujo R, Ferreira T A E. An intelligent hybrid morphological-rank-linear method for financial time series prediction[J]. Neurocomputing, 2009, 72(10/11/12): 2507-2524.
[14]  (Yang Z M, Yue J G, Wang X B, et al. Prediction of urban rail transit power consumption based on regression model[J]. Urban Mass Transit, 2010, 13(12): 22-25.)
[15]  Omer F D. A hybrid neural network and ARIMA model for water quality time series prediction[J]. Engineering Applications of Artificial Intelligence, 2010, 23(4): 586-594.
[16]  徐惠莉, 吴柏林, 江韶珊. 区间时间序列预测准确度探讨[J]. 数量经济技术经济研究, 2008, 12(1): 133-140.
[17]  (Xu H L, Wu B L, Jiang S S. On forecasting efficiency evaluation for interval time series[J]. The J of Quantitative & Technical Economics, 2008, 12(1): 133-140.)
[18]  Kaastra I, Boyd M. Designing a neural network for forecasting financial and economic time-series[J]. Neurocomputing, 1996, 10(3): 215-236.
[19]  杨臻明, 岳继光, 王晓保, 等. 基于回归模型的城市轨道交通能耗预测[J]. 城市轨道交通研究, 2010, 13(12): 22-25.

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