×
Ye, Jing; Li, Yan; Ma, Zhenzhen; Xiong, Pingping

Novel weight-adaptive fusion grey prediction model based on interval sequences and its applications. (English) Zbl 1510.62376

Appl. Math. Modelling 115, 803-818 (2023).

MSC:

62M20 Inference from stochastic processes and prediction
90C59 Approximation methods and heuristics in mathematical programming

Keywords:

GM(1,N) model; weight adaptation; interval sequences; PSO algorithm
Cite Review PDF
Full Text: DOI

References:

[1] Liu, S. F.; Lin, Y., Grey Systems: Theory And Applications (2010), Springer-Verlag London Ltd · Zbl 1222.93001
[2] Wang, Z. X.; Hao, P., An improved grey multivariable model for predicting industrial energy consumption in China, Appl. Math. Model., 40, 11-12, 5745-5758 (2016)
[3] Xie, M.; Wu, L. F.; Li, B., A novel hybrid multi-variate nonlinear grey model for forecasting the traffic-related emissions, Appl. Math. Modell., 77, 1242-1254 (2020) · Zbl 1481.91146
[4] Duan, H. M.; Wang, D.; Pang, X. Y.; Liu, Y.; Zeng, S., A novel forecasting approach based on multi-kernel nonlinear multivariable grey model: A case report, J. Cleaner Prod., 260 (2020)
[5] Ma, X.; Xie, M.; Wu, W., The novel fractional discrete multivariate grey system model and its applications, Appl. Math. Modell., 70, 402-424 (2019) · Zbl 1464.62389
[6] Zeng, B.; Luo, C. M.; Liu, S. F.; Bai, Y.; Li, C., Development of an optimization method for the GM(1,N) model, Eng. Appl. Artif. Intell., 55, 353-362 (2016)
[7] Mao, S. H.; Gao, M. Y.; Xiao, X. P., Fractional order accumulation time-lag GM(1,N, τ) model and its application, Syst. Eng. Theor. Pract., 35, 430-436 (2015)
[8] Ding, S.; Dang, Y. G.; Xu, N., Construction and application of GM(1,N) model based on virtual variable control, Control Decis., 33, 309-315 (2018) · Zbl 1413.93003
[9] Guo, H.; Xiao, X. P.; Forrest, J., A research on a comprehensive adaptive grey prediction model CAGM(1,N), Appl. Math. Comput., 225, 216-227 (2013) · Zbl 1336.93011
[10] Jiang, S. Q.; Liu, S. F., Optimization of background value in GM(1,1) based on compound trapezoid formula, Control Decis., 29, 2221-2225 (2014)
[11] Hsu, L. C., Forecasting the output of integrated circuit industry using genetic algorithm based multivariable grey optimization models, Expert Syst. Appl., 36, 7898-7903 (2009)
[12] Lao, T.; Chen, X.; Zhu, J., The optimized multivariate grey prediction model based on dynamic background value and its application, Complexity, 2021, 1-13 (2021)
[13] Ding, S.; Dang, Y. G.; Xu, N., Construction and optimization of approximate nonhomogeneous exponential decreasing sequence NGO (1,1) model, Control Decis., 32, 1457-1464 (2017) · Zbl 1399.93010
[14] Zhao, L. D.; Wang, H. X., Research on non-equidistant multivariable grey prediction model based on initial condition optimization, Fuzzy Syst. Math., 33, 136-142 (2019) · Zbl 1438.93134
[15] Xiong, P. P.; Dang, Y. G.; Yao, T. X., A non-equidistant GM (1,1) modeling method based on initial condition optimization, Control Decis., 30, 2097-2102 (2015) · Zbl 1349.93056
[16] Wang, Y.; Nie, R.; Ma, X., A novel Hausdorff fractional NGMC(p,n) grey prediction model with Grey Wolf Optimizer and its applications in forecasting energy production and conversion of China, Appl. Math. Modell., 97, 381-397 (2021) · Zbl 1481.91132
[17] Yin, F. F.; Zeng, B., A novel multivariable grey prediction model with different accumulation orders and performance comparison, Appl. Math. Modell., 109, 117-133 (2022) · Zbl 1505.62517
[18] Luo, Y.; Liu, Q., The non-homogenous multi-variable grey model nfmgm (1,N) with fractional order accumulation and its application, Grey Syst., 29, 39-52 (2017)
[19] Ye, J.; Dang, Y.; Ding, S., A novel energy consumption forecasting model combining an optimized DGM(1,1) model with interval grey numbers, J. Cleaner Prod., 229, 256-267 (2019)
[20] Yang, Y.; Xue, D. Y., An actual load forecasting methodology by interval grey modeling based on the fractional calculus, ISA Trans., 82, 200-209 (2018)
[21] Zeng, X. Y.; Shu, L.; Jiang, J., Fuzzy time series forecasting based on grey model and Markov Chain, IAENG Int. J. Appl. Math., 46, 464-472 (2016) · Zbl 1512.62083
[22] Wu, L. F.; Liu, S. F.; Yan, S. L., Grey prediction model for hybrid sequence, Control Decis., 28, 1912-1914 (2013)
[23] Huang, H. L.; Tao, Z. F.; Liu, J. P., Exploiting fractional accumulation and background value optimization in multivariate interval grey prediction model and its application, Eng. Appl. Artif. Intell., 104, Article 104360 pp. (2021)
[24] Li, S. L.; Zeng, B.; Meng, W., Unbiased interval grey number prediction model based on Clem rule and its application, Control Decis., 33, 2258-2262 (2018) · Zbl 1424.93014
[25] Xiong, T.; Bao, Y. K.; Hu, Z. Y., Interval forecasting of electricity demand: a novel bivariate EMD-based support vector regression modeling framework, Int. J. Electr. Power Energy Syst., 63, 353-362 (2014)
[26] Yadav, N.; Chakraborty, N.; Tewari, A., Interval prediction machine learning models for predicting experimental thermal conductivity of high entropy alloys, Comput. Mater. Sci., 214, Article 111754 pp. (2022)
[27] Zeng, X. Y.; Wang, M. Y.; He, F. L., Matrix grey models for forecasting interval number time series, J. Grey Syst., 31, 72-90 (2019)
[28] Zeng, X. Y.; Yan, S. L.; He, F. L.; Shi, Y., Multi-variable grey model based on dynamic background algorithm for forecasting the interval sequence, Appl. Math. Modell., 80, 99-114 (2020) · Zbl 1481.62075
[29] Zeng, X. Y.; Shu, L.; Yan, S. L., A novel multivariate grey model for forecasting the sequence of ternary interval numbers, Appl. Math. Model., 69, 273-286 (2019) · Zbl 1464.62525
[30] Liu, S. F.; Dang, Y. G.; Fang, Z. G., Grey System Theory and Application (2010), Science Press
[31] Luo, D.; Liu, S. F.; Dang, Y. G., Grey model GM(1,1) optimization, Chinese, Eng. Sci., 5, 50-53 (2003)
[32] Liu, S. F.; Yang, Y. J.; Forrest, J., Grey Data Analysis: Methods, Models and Applications (2016), Springer-Verlag
[33] Liu, J. F.; Liu, S. F.; Fang, Z. G., Prediction model of continuous interval grey number based on the kernel and grey radius, Syst. Eng., 31, 61-64 (2013)
[34] Zeng, B.; Li, C., Improved multi-variable grey forecasting model with a dynamic background value coefficient and its application, Comput. Ind. Eng., 118, 278-290 (2018)
[35] Zeng, B.; Li, H.; Ma, X., A novel multi-variable grey forecasting model and its application in forecasting the grain production in China, Comput. Ind. Eng., 150, Article 106915 pp. (2020)
[36] Liu, Z.; Dang, Y. G.; Wei, L., Optimization of background value and time response function in NGM(1,1,k), Control Decis., 31, 2225-2231 (2016) · Zbl 1389.93022
[37] Ding, S.; Li, R. J.; Tao, Z., A novel adaptive discrete grey model with time- varying parameters for long-term photovoltaic power generation forecasting, Energy Convers. Manag., 227, Article 113644 pp. (2021)
[38] Zhang, M.; Guo, H.; Sun, M., A novel flexible grey multivariable model and its application in forecasting energy consumption in China, Energy, 239, Article 122441 pp. (2022)
[39] Xiao, X. P.; Mao, SH., Grey Prediction and Decision-Making Methods (2013), Science Press: Science Press Beijing
[40] Xu, N.; Dang, Y.; Gong, Y., Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in, China Energy, 118, 473-480 (2017)
[41] Special topic: nutritional dietary guidance for the prevention and treatment of pneumonia caused by novel coronavirus infection, Food Life, 2, 4-5 (2020)
[42] Editorial committee of china dairy yearbook, China Dairy Yearbook 2020 (2021), China Agriculture Press
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.