[1] 王迪,王萍,石君志.一种错误率可控的混沌时间序列区间预测算法[J].控制与决策,2019,034(005):956-964. [2] Pan W, Feng L, Zhang L, et al. Time-series interval prediction under uncertainty using modified double multiplicative neuron network[J]. Expert Systems with Applications, 2021, 184(1): 115478. [3] 万昆,柳瑞禹.区间时间序列向量自回归模型在短期电力负荷预测中的应用[J].电网技术,2012,11:77-81. [4] Pellegrini S, Ruiz E, Espasa A. Prediction intervals in conditionally heteroscedastic time series with stochastic components[J]. International Journal of Forecasting, 2011, 27(2): 308-319. [5] Cheung Y W. An empirical model of daily highs and lows[J]. International Journal of Finance & Economics, 2010, 12(1): 1-20. [6] Maia A L S, Carvalho F A T. Holt's exponential smoothing and neural network models for forecasting interval-valued time series [J]. International Journal of Forecasting, 2011, 27(3): 740-759. [7] Xiong T, Bao Y K, Hu Z Y. Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms[J]. Information Sciences, 2015, 305: 77-92. [8] Xiong T, Li C, Bao Y. Interval-valued time series forecasting using a novel hybrid holtI and MSVR model[J]. Economic Modelling, 2017, 60: 11-23. [9] 陈俊风,王玉浩,张学武,等.基于小波变换与差分变异BSO-BP算法的大坝变形预测[J].控制与决策,2020,36(07):1611-1618. [10] Reboredo J C, Rivera-Castro M A. A wavelet decomposition approach to crude oil price and exchange rate dependence[J]. Economic Modelling, 2013, 32(32): 42-57. [11] 崔焕影,窦祥胜.基于EMD-GA-BP与EMD-PSO-LSSVM的中国碳市场价格预测[J].运筹与管理,2018,027(007):133-143. [12] Dragomiretskiy K, Zosso D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544. [13] Ma X, Jin Y, Dong Q. A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting[J]. Applied Soft Computing, 2017, 54: 296-312. [14] 王珏,齐琛,李明芳.基于SSA-ELM的大宗商品价格预测研究[J].系统工程理论与实践,2017,8:2004-2014. [15] Abdollahzade M, Miranian A, Hassani H, et al. A new hybrid enhanced local linear neuro-fuzzy model based on the optimized singular spectrum analysis and its application for nonlinear and chaotic time series forecasting[J]. Information Sciences, 2015, 295: 107-125. [16] Sun S L, Suny Y,Wang S Y, Wei Y J. Interval delomposition ensemble opproach for cwudeoil hsice forecasting[J]. Enesgy Economics, 2018, 26: 274-287. |