Monitoring and Prediction of Time Series Based on Fuzzy Cognitive Maps with Multi-step Gradient Methods

被引：5
|
作者：
Poczeta, Katarzyna [1 ]
Yastrebov, Alexander [1 ]
机构：
[1] Kielce Univ Technol, Al Tysiaclecia Panstwa Polskiego 7, PL-25314 Kielce, Poland
来源：
PROGRESS IN AUTOMATION, ROBOTICS AND MEASURING TECHNIQUES: CONTROL AND AUTOMATION | 2015年 / 350卷
关键词：
fuzzy cognitive map; multi-steps algorithms; gradient method; Markov model of gradient; monitor system; time series prediction; LEARNING ALGORITHM;
D O I：
10.1007/978-3-319-15796-2_20
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Fuzzy cognitive map FCM is a useful tool for modeling systems for time series monitoring and prediction in various fields. This paper is devoted to the analysis of the application of FCM with multistep learning algorithms based on gradient method and Markov model of gradient for multivariate time series monitoring and prediction. Real data from a monitor system mounted in a domotic house were used in learning and testing process. The comparative analysis of two-step method of Markov model of gradient, multi-step gradient method and one-step gradient method from the point of view of the obtained prediction error was performed.
引用
收藏
页码：197 / 206
页数：10
相关论文
共 50 条
  • [1] Fuzzy Cognitive Maps and Multi-step Gradient Methods for Prediction: Applications to Electricity Consumption and Stock Exchange Returns
    Papageorgiou, Elpiniki I.
    Poczeta, Katarzyna
    Yastrebov, Alexander
    Laspidou, Chrysi
    INTELLIGENT DECISION TECHNOLOGIES, 2015, 39 : 501 - 511
  • [2] Analysis of multi-step algorithms for cognitive maps learning
    Jastriebow, A.
    Poczeta, K.
    BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES, 2014, 62 (04) : 735 - 741
  • [3] A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks
    Papageorgiou, Elpiniki I.
    Poczeta, Katarzyna
    NEUROCOMPUTING, 2017, 232 : 113 - 121
  • [4] Deep Fuzzy Cognitive Maps for Interpretable Multivariate Time Series Prediction
    Wang, Jingyuan
    Peng, Zhen
    Wang, Xiaoda
    Li, Chao
    Wu, Junjie
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2021, 29 (09) : 2647 - 2660
  • [5] Evaluation of Deep Learning Models for Multi-Step Ahead Time Series Prediction
    Chandra, Rohitash
    Goyal, Shaurya
    Gupta, Rishabh
    IEEE ACCESS, 2021, 9 : 83105 - 83123
  • [6] Multi-Step Prediction Algorithm of Traffic Flow Chaotic Time Series based on Volterra Neural Network
    Yin, Lisheng
    He, Yigang
    Dong, Xueping
    Lu, Zhaoquan
    JOURNAL OF COMPUTERS, 2013, 8 (06) : 1480 - 1487
  • [7] Analysis of Fuzzy Cognitive Maps with Multi-Step Learning Algorithms in Valuation of Owner-Occupied Homes
    Poczeta, Katarzyna
    Yastrebov, Alexander
    2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2014, : 1029 - 1035
  • [8] A multi-step regularity assessment and joint prediction system for ordering time series based on entropy and deep learning
    Zhou, Yichen
    Han, Wenhe
    Zhou, Heng
    Autonomous Intelligent Systems, 2024, 4 (01):
  • [9] Time series prediction based on high-order intuitionistic fuzzy cognitive maps with variational mode decomposition
    Yao Xixi
    Ding Fengqian
    Luo Chao
    Soft Computing, 2022, 26 : 189 - 201
  • [10] Time series prediction based on high-order intuitionistic fuzzy cognitive maps with variational mode decomposition
    Yao Xixi
    Ding Fengqian
    Luo Chao
    SOFT COMPUTING, 2022, 26 (01) : 189 - 201
12345