Higher order multivariate Markov chain model for fuzzy time series

被引:11
|
作者
Suresh, S. [1 ]
Kannan, K. Senthamarai [2 ]
Venkatesan, P. [3 ]
机构
[1] Univ Madras, Dept Stat, Madras 5, Tamil Nadu, India
[2] Manonmaniam Sundaranar Univ, Dept Stat, Tirunelveli, Tamil Nadu, India
[3] ICMR, Natl Inst Res TB, Dept Stat, Madras, Tamil Nadu, India
来源
关键词
Fuzzy sets; Markov model; Higher order Markov model; Linear programming problem and simulation;
D O I
10.1080/09720510.2014.894303
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Time series analysis is often related with the discovery of patterns and prediction of features. In this paper, multivariate Markov model in fuzzy time series method to allow multi factor forecasting problem is proposed. An attempt is made to forecast global surface temperature with presence of CO2 emission. The computation is carried out using fuzzy sets and transition probability vectors. A time series model has been determined by applying Fuzzy logic. Reliability of the randomness of the forecast values have been studied by implementing random number simulation technique. Numerical computations have been carried out in support of theoretical findings.
引用
收藏
页码:21 / 35
页数:15
相关论文
共 50 条
  • [41] Optimal determination of hidden Markov model parameters for fuzzy time series forecasting
    Salawudeen, Ahmed T.
    Mu'azu, Muhammed B.
    Adedokun, Emmanuel A.
    Baba, Bashir A.
    [J]. SCIENTIFIC AFRICAN, 2022, 16
  • [42] A prediction model of uncertain time series based on high-order Markov model
    Zhou, Chunnan
    Huang, Shaobin
    Chi, Ronghua
    Cheng, Yuan
    [J]. Journal of Computational Information Systems, 2014, 10 (08): : 3237 - 3246
  • [43] A novel fuzzy-Markov forecasting model for stock fluctuation time series
    Guan, Hongjun
    Jie, He
    Guan, Shuang
    Zhao, Aiwu
    [J]. EVOLUTIONARY INTELLIGENCE, 2020, 13 (02) : 133 - 145
  • [44] First Order Non-homogeneous Markov Chain Model for Generation of Wind Speed and Direction Synthetic Time Series
    Di Giorgio, V
    Langella, R.
    Testa, A.
    Djokic, S. Z.
    Zou, M.
    [J]. 2020 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS), 2020,
  • [45] Test to determine the Markov order of a time series
    Racca, E.
    Laio, F.
    Poggi, D.
    Ridolfi, L.
    [J]. PHYSICAL REVIEW E, 2007, 75 (01):
  • [46] A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction
    Vlasenko, Alexander
    Vlasenko, Nataliia
    Vynokurova, Olena
    Peleshko, Dmytro
    [J]. DATA, 2018, 3 (04)
  • [47] An improved multivariate Markov chain model for credit risk
    Ching, Wai-Ki
    Siu, Tak-Kuen
    Li, Li-min
    Jiang, Hao
    Li, Tang
    Li, Wai-Keung
    [J]. JOURNAL OF CREDIT RISK, 2009, 5 (04): : 83 - 106
  • [48] A New Improved Parsimonious Multivariate Markov Chain Model
    Wang, Chao
    Huang, Ting-Zhu
    [J]. JOURNAL OF APPLIED MATHEMATICS, 2013,
  • [49] On a multivariate Markov chain model for credit risk measurement
    Siu, TK
    Ching, WK
    Fung, ES
    Ng, MK
    [J]. QUANTITATIVE FINANCE, 2005, 5 (06) : 543 - 556
  • [50] Stochastic Time Series Analysis for Energy System Based on Markov Chain Model
    Zhengshun Ruan
    Aihua Luo
    Hong Yao
    [J]. Mobile Networks and Applications, 2017, 22 : 427 - 434