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A Weighted Fuzzy Time Series Forecasting Model

Daniel Ortiz-Arroyo, Jens Runi Poulsen
2018 Indian Journal of Science and Technology  
The fuzzy sets extracted from our partitioning are grouped to create a rule-base that will be used in forecasting.  ...  In this paper we describe a new automatic partitioning method and a first order weighted fuzzy time series forecasting model.  ...  To address with these issues, Chen and Chung in 7 proposed a high order fuzzy time series model that uses genetic algorithms to optimize the fuzzy interval lengths.  ... 
doi:10.17485/ijst/2018/v11i27/130708 fatcat:dihlfampune6piermz5arjgi4m

Fuzzy Supervised Multi-Period Time Series Forecasting

Galina Ilieva
2019 Cybernetics and Information Technologies  
The goal of this paper is to propose a new method for fuzzy forecasting of time series with supervised learning and k-order fuzzy relationships.  ...  The proposed algorithm is verified by a benchmark dataset for fuzzy time series forecasting. The results obtained are similar or better than those of other fuzzy time series prediction methods.  ...  C h a n g, W e i and C h e n g [1] propose a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS) model that employs AR and volatility to solve stock price forecast problems. C h e n g et al  ... 
doi:10.2478/cait-2019-0016 fatcat:t54tj532srgnxd5vutqmhmjmrm

Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line

Majid Jamil, Abul Kalam, A.Q. Ansari, M. Rizwan
2014 Applied Soft Computing  
Ansari, "Employing Genetic Algorithm to Optimize OWA-Fuzzy Forecasting Model," Proc. IEEE Int. Conf. on Systems -Man and Cybernetics, Spain, Oct. 19-21, 2011. 37. Shabana Urooj, A.  ...  Ansari, "Employing OWA to Optimize Fuzzy Predictor," Proc. IEEE World Conference on Soft Computing (WConSC 2011), San Francisco, USA, pp. 205 -211, May 23 -26, 2011. 42. M. A. Khan, A. Q.  ... 
doi:10.1016/j.asoc.2014.02.020 fatcat:3mdf5egtyfgu5cnswxtkbptpou

Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting

Yi-Chung Hu
2017 Sustainability  
Therefore, in this study, we employ the FLN with a fuzzy integral instead of an inner product to propose a nonadditive FLNGM(1,1).  ...  The GM(1,1) model has drawn our attention to energy demand forecasting because it only needs a few data points to construct a time series model without statistical assumptions.  ...  Acknowledgments: The author would like to thank the anonymous referees for their valuable comments.  ... 
doi:10.3390/su9071166 fatcat:j53lmomgqfhlrc2tbmuc2phtza

On the Uniform Convergence of the Orthogonal Series-Type Kernel Regression Neural Networks in a Time-Varying Environment [chapter]

Meng Joo Er, Piotr Duda
2012 Lecture Notes in Computer Science  
Krzysztof Pa,tan. and, Jozef Korbicz Variable Selection in the Kernel Regression Based Short-Term Load Forecasting Model 557 Software Modeling Language with Frames and Multi-abstractions: An Overview 564  ...  Dorota Cendrowska. and Katarzyna W §grzyn-Wolska Determining OWA Operator Weights by Mean Absolute Deviation Minimization 283 Michal Majdan and Wlodzimierz Ogryczak Efficient MPC Algorithms Based  ... 
doi:10.1007/978-3-642-29347-4_5 fatcat:pqwbzeuqsbg6lkhlmmrsj6qb24

A Study of Different Existing Methods for the Stock Selection in the Field of Quantitative Investment

Pengfei Li, Jungang Xu, Mohammad Farukh Hashmi
2022 Wireless Communications and Mobile Computing  
more stable and efficient stock selection models.  ...  In particular, with the development of artificial intelligence technology, an increasing number of researchers try to apply different machine learning and deep learning methods to this field to obtain  ...  In the evolutionary model, genetic algorithm was used to optimize the model parameters and select input variables' features.  ... 
doi:10.1155/2022/2695099 fatcat:l7l7ov2z3zeuleorvt2emzcxrm

A soft computing-based approach to spatio-temporal prediction

Rúbia E.O. Schultz, Tania M. Centeno, Gilles Selleron, Myriam R. Delgado
2009 International Journal of Approximate Reasoning  
The proposal here is to present a spatio-temporal prediction method of forestry evolution for a sequence of binary images by means of fuzzy inference systems (FIS), genetic algorithm (GA) and genetic programming  ...  The fuzzy system is formed by a fixed fuzzy rule base and a partition set that may be defined by an expert or optimized by means of a GA.  ...  In the second case, a genetic algorithm can be used to optimize the membership functions of the first set of fuzzy rules, i.e. the data base in the MIM.  ... 
doi:10.1016/j.ijar.2008.01.010 fatcat:6v457zeyvffubggoua7ryzsxqu

A Modified Functional Link Neural Network for Data Classification [chapter]

Toktam Babaei, Chee Peng Lim, Hamid Abdi, Saeid Nahavandi
2017 Series in BioEngineering  
This research focuses on using different FLNN-based models to tackle data classification tasks.  ...  To further improve the robustness of rFLNN2 an ensemble of multiple rFLNN2-based models is formulated.  ...  Then, the BP algorithm is employed for weight tuning.  ... 
doi:10.1007/978-981-10-3957-7_13 fatcat:lfjfchicyfeqvlprvrqpp4op5e

Literature Review of various Fuzzy Rule based Systems [article]

Ayush K. Varshney, Vicenç Torra
2022 arXiv   pre-print
In this paper, we present an overview and literature review for various types and prominent areas of fuzzy systems (FRBSs) namely genetic fuzzy system (GFS), Hierarchical fuzzy system (HFS), neuro fuzzy  ...  Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent the human understandable knowledge.  ...  Data availability: Data sharing not applicable to this article as no datasets were generated or analysed during the current study.  ... 
arXiv:2209.07175v1 fatcat:zs6qimx4czhqhenk4phiasvkxa

Linguistic Summarization of Time Series Data using Genetic Algorithms

Rita Castillo-Ortega, Nicolas Marin, Daniel Sanchez, Andrea G.B. Tettamanzi
2011 Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)  
The number of possible final summaries and the different ways of measuring their quality has taken us to adopt the use of a multi objective evolutionary algorithm.  ...  We compare the results of the new approach with our previous greedy algorithms.  ...  level employed in the graphical depiction.  ... 
doi:10.2991/eusflat.2011.145 dblp:conf/eusflat/Castillo-OrtegaMST11 fatcat:khqiliqz7naehjz2wfmskmyxau


Keyu Lu, Huchang Liao, Edmundas Kazimieras Zavadskas
2021 Technological and Economic Development of Economy  
In this regard, many scholars applied fuzzy techniques to solve decision making problems in SCM.  ...  However, due to the complexity of evaluated objects and the cognitive limitations of individuals, the decision information given by experts is often fuzzy, which may make it difficult to make decisions  ...  Disclosure statement The authors have no competing financial, professional, or personal interests from other parties that are related to this paper.  ... 
doi:10.3846/tede.2021.14433 fatcat:62hlbeiukbeplnfnwxehbevs5y

Analyzing the Efficiency of Travel and Tourism in the European Union [chapter]

Petra Barišić, Violeta Cvetkoska
2019 Springer Proceedings in Business and Economics  
Abstract: A simulation model to determine a runway exit location is proposed in the paper.  ...  An algorithm has been presented to find the optimal solution.  ...  The robustness of the proposed methodology is evaluated using OWA combinations involving different minimax disparity models and different levels of optimism of the decision maker.  ... 
doi:10.1007/978-3-030-21990-1_10 fatcat:mkkhegc3hjgzzfra66ccabhw44

Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions

Ankit Thakkar, Kinjal Chaudhari
2020 Information Fusion  
information fusion, feature fusion, and model fusion.  ...  The investors' etiquettes towards stock market may demand the need of studying various associated factors and extract the useful information for reliable forecasting.  ...  Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper  ... 
doi:10.1016/j.inffus.2020.08.019 pmid:32868979 pmcid:PMC7448965 fatcat:ji7va4kekjh4tgclotgg7na7sa

Fuzzy multiple criteria decision making: Recent developments

Christer Carlsson, Robert Fullér
1996 Fuzzy sets and systems (Print)  
Fuzzy MCDM has basically been developed along the same lines, although with the help of fuzzy set theory a number of innovations have been made possible; the most important methods are reviewed and a novel  ...  Buckley and Hayashi [12] introduced fuzzy genetic algorithms to (approximately) solve fuzzy optimization problems.  ...  Fuzzy genetic algorithms look like an interesting method of producing approximate solutions to fuzzy optimization problems when the variables can be arbitrary discrete fuzzy subsets of certain intervals  ... 
doi:10.1016/0165-0114(95)00165-4 fatcat:zwyxob2n7nepnm64wgqeppigbi

Computational Intelligence Based Complex Adaptive System-of-System Architecture Evolution Strategy [chapter]

Siddhartha Agarwal, Cihan H. Dagli, Louis E. Pape
2015 Complex Systems Design & Management  
Metaarchitectures are generated using evolutionary algorithms and assessed using type II fuzzy nets.  ...  To address this issue a negotiation model is proposed which helps the SoS manger to adapt his strategy based on system owners behavior.  ...  Besides the multiple waves depend on scenario for simulation and hence different domains may lead to different results.  ... 
doi:10.1007/978-3-319-26109-6_9 dblp:conf/csdm/AgarwalDP15 fatcat:j3uqb2unfzgl3dsk46d7xjsctu
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