Time series forecasting using fuzzy cognitive maps: a survey

Orang, Omid; Silva, Petrônio Cândido de Lima e; Guimarães, Frederico Gadelha

doi:10.1007/s10462-022-10319-w

Cited by 32 publications

(5 citation statements)

References 183 publications

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“…Creating an FCM involves a structured and iterative process encompassing problem definition, concept identification, causal relationship elicitation, initial state assignment, rule specification, FCM construction, learning algorithm selection, training and validation, model output and interpretation, and model refinement [17,18]. The pipeline (Figure 2) ensures that FCMs are designed effectively to represent the dynamics and relationships within complex systems, enabling accurate predictions, decision-making, and knowledge extraction.…”

Section: From Theory To Real-world Scenariosmentioning

confidence: 99%

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

Apostolopoulos,

Papandrianos,

Papathanasiou

et al. 2024

Bioengineering

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Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored.

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Section: From Theory To Real-world Scenariosmentioning

confidence: 99%

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

Apostolopoulos,

Papandrianos,

Papathanasiou

et al. 2024

Bioengineering

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“…m denotes the value of the m-th concept at p-th iteration step, w ji denotes the causal weight from j-th concept to i-th concept, and f (•) denotes the activation function that normalizes the concepts' activation values within a specified interval [12]. The most known activation functions are bivalent, trivalent, hyperbolic tangent, and sigmoid, where depending on which is selected, A (p) m receives values within the [0, 1] or [−1, 1] intervals [25]. The activation values of all concepts in each iteration step can be expressed as a state vector A ∈ R n , while the values of the causal weights w ij between each pair of concepts C i and C j , compose a weight matrix W ∈ R n×n , whose diagonal elements are equal to zero.…”

Section: A Fuzzy Cognitive Mapsmentioning

confidence: 99%

Information Flow-Based Fuzzy Cognitive Maps with Enhanced Interpretability

Tyrovolas¹,

Liang²,

Stylios³

2023

Preprint

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<p>A Fuzzy Cognitive Map (FCM) is a graph-based tool for knowledge representation that intends to model any complex system through an interactive structure of nodes interacting with each other through causal relationships. Owing to their flexibility and inherent interpretability, FCMs have been used in various modeling and prediction tasks, particularly in situations where humans make final decisions, such as industrial anomaly detection. However, FCMs can unintentionally absorb spurious correlations presented in collected data during development, leading to poor prediction accuracy and interpretability. To address this limitation, this article proposes a novel framework for constructing FCMs based on the Liang-Kleeman Information Flow (L-K IF) analysis, a causal inference tool. The actual causal relationships are identified from the data using an automatic causal search algorithm, and these are then imposed as constraints in the FCM learning procedure to rule out spurious correlations and improve the predictive and explanatory power of the model. Numerical simulations were conducted by comparing the proposed approach with state-of-the-art FCM-based models, thereby demonstrating the promising performance of the developed FCM.</p>

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“…O Sistema de Inferência Fuzzy (SIF) é baseado nos conceitos de conjuntos, regras e raciocínio fuzzy. As regras se mostram eficientes na modelagem de proposic ¸ões em linguagem natural e esses sistemas podem ser encontrados de forma isolada ou combinada com outros métodos [Das et al 2022] em aplicac ¸ões em áreas de classificac ¸ão, regressão e agrupamento de dados [Skrjanc et al 2019], predic ¸ão de séries temporais [Orang et al 2022], tomada de decisões [Bisht and Kumar 2022] entre outros. O SIF é um modelo que faz predic ¸ões numéricas a partir dos dados de entrada e, por esse motivo é utilizado neste trabalho como um método de regressão.…”

Section: Sistemas De Inferência Fuzzyunclassified

Uma Estratégia para Alocação de Carteira de Ações usando Algoritmos de Aprendizado de Máquina e Regras Fuzzy

Gambim,

Camargo,

Lucca

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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No mercado de ações, investidores interessados em investimentos de longo prazo buscam formas de selecionar ações com potencial de valorização futura para melhorar seus rendimentos. Os métodos de aprendizado de máquina têm um longo histórico de aplicações no mercado financeiro. Este artigo apresenta e avalia uma estratégia simples e eficaz para alocação de carteiras de ações, que utiliza algoritmos de aprendizado de máquina para prever o desempenho das ações e selecionar algumas delas para compor a carteira. O método proposto é particularmente útil para investidores com pouco conhecimento do mercado. Os resultados obtidos mostram que a estratégia proposta levou à alocação de carteiras com lucros superiores aos do índice de mercado S&P500 e aos de um método manual conhecido na literatura.

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Time series forecasting using fuzzy cognitive maps: a survey

Cited by 32 publications

References 183 publications

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

Information Flow-Based Fuzzy Cognitive Maps with Enhanced Interpretability

Uma Estratégia para Alocação de Carteira de Ações usando Algoritmos de Aprendizado de Máquina e Regras Fuzzy

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