Fuzzy Cognitive Maps Employing ARIMA Components for Time Series Forecasting

2017 ◽  
pp. 255-264 ◽  
Author(s):  
Frank Vanhoenshoven ◽  
Gonzalo Nápoles ◽  
Samantha Bielen ◽  
Koen Vanhoof
Keyword(s):  
Time Series ◽  
Cognitive Maps ◽  
Time Series Forecasting ◽  
Fuzzy Cognitive Maps

Pseudoinverse learning of Fuzzy Cognitive Maps for multivariate time series forecasting

2020 ◽  
Vol 95 ◽  
pp. 106461
Author(s):  
Frank Vanhoenshoven ◽  
Gonzalo Nápoles ◽  
Wojciech Froelich ◽  
Jose L. Salmeron ◽  
Koen Vanhoof
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Cognitive Maps ◽  
Time Series Forecasting ◽  
Fuzzy Cognitive Maps

Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps

2020 ◽  
Vol 206 ◽  
pp. 106359
Author(s):  
Kaixin Yuan ◽  
Jing Liu ◽  
Shanchao Yang ◽  
Kai Wu ◽  
Fang Shen
Keyword(s):  
Time Series ◽  
Cognitive Maps ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Cognitive Maps ◽  
Kernel Mapping

Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference Based on Time Series Data

2021 ◽  
Author(s):  
Marzieh Emadi ◽  
Farsad Zamani Boroujeni ◽  
jamshid Pirgazi
Keyword(s):  
Time Series ◽  
Compressed Sensing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Cognitive Maps ◽  
Boolean Networks ◽  
Fuzzy Cognitive Maps ◽  
Series Data ◽  
Gene Regulatory

Abstract Recently with the advancement of high-throughput sequencing, gene regulatory network inference has turned into an interesting subject in bioinformatics and system biology. But there are many challenges in the field such as noisy data, uncertainty, time-series data with numerous gene numbers and low data, time complexity and so on. In recent years, many research works have been conducted to tackle these challenges, resulting in different methods in gene regulatory networks inference. A number of models have been used in modeling of the gene regulatory networks including Boolean networks, Bayesian networks, Markov model, relational networks, state space model, differential equations model, artificial neural networks and so on. In this paper, the fuzzy cognitive maps are used to model gene regulatory networks because of their dynamic nature and learning capabilities for handling non-linearity and inherent uncertainty. Fuzzy cognitive maps belong to the family of recurrent networks and are well-suited for gene regulatory networks. In this research study, the Kalman filtered compressed sensing is used to infer the fuzzy cognitive map for the gene regulatory networks. This approach, using the advantages of compressed sensing and Kalman filters, allows robustness to noise and learning of sparse gene regulatory networks from data with high gene number and low samples. In the proposed method, stream data and previous knowledge can be used in the inference process. Furthermore, compressed sensing finds likely edges and Kalman filter estimates their weights. The proposed approach uses a novel method to decrease the noise of data. The proposed method is compared to CSFCM, LASSOFCM, KFRegular, ABC, RCGA, ICLA, and CMI2NI. The results show that the proposed approach is superior to the other approaches in fuzzy cognitive maps learning. This behavior is related to the stability against noise and offers a proper balance between data error and network structure.

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