Qualitative Upper and Lower Approximations of Complex Nonlinear Chaotic and Nonchaotic Models

Dohnal, Mirko; Doubravský, Karel

doi:10.1142/s0218127415501734

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…2 5,41% (Figini Giudici, 2011) (Jurasz Mikulik, 2016) (Costa et al;2016) (Annarelli et al;2018) (Elwakil Zayed, 2015) (Qu Zhou, 2017) (Kokodey, 2012) (Hallah Aboukhamseen, 2019) (Varga et al;2008) (Pérez-Rodríguez, 2011) (Dohnal Doubravsky, 2015) Tabela 3 -Principais lacunas identificadas pelos autores dos artigos coletados (conclusão)…”

Section: Lacunas Identificadas Nº De Artigos Autoresmentioning

confidence: 99%

Séries temporais e variáveis qualitativas

Senna

Souza²,

Ueda³

2023

Revista PO: R. Eletr. de Eng. de Produção e Correlatas

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As variáveis qualitativas possuem características de influenciar variáveis quantitativas. As séries temporais são exemplo de variáveis quantitativas que podem ser modificadas por alterações abruptas de variáveis qualitativas. O objetivo desta pesquisa é identificar uma lacuna de conhecimento entre modelos de previsão de séries temporais e variáveis qualitativas por meio deuma revisão sistemática de literatura aplicada em artigos científicos publicados em bases internacionais. Foram encontrados 37 artigos que unem séries temporais e variáveis qualitativas no período de 1985 a 2020, nos quais novos modelos foram criados utilizaram as variáveis qualitativas como entradas de informação para a geração de previsões. A lacuna encontrada foi em aproximados 57% dos casos a capacidade de melhoria dos modelos criados e a aplicação em outros sistemas, e em 19% a possibilidade de melhorar a previsão de outras variáveis do sistema. Os resultados indicaram que a inclusão de variáveis qualitativas em modelos quantitativos auxilia na qualidade da precisão das previsões geradas.

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Section: Lacunas Identificadas Nº De Artigos Autoresmentioning

confidence: 99%

Séries temporais e variáveis qualitativas

Senna

Souza²,

Ueda³

2023

Revista PO: R. Eletr. de Eng. de Produção e Correlatas

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“…By substituting (2) into the relations describing the trend model, it can be determined whether there is no conflict with the meaning of any of these relations. The result is a set of scenarios ( Dohnal & Doubravsky, 2015;Doubravský, 2019;Doubravsky & Dohnal, 2018).…”

Section: Trend Analysismentioning

confidence: 99%

“…For each scenario in Table 6, the relevant equations are determined using the qualitative sum and product. (Dohnal & Doubravsky, 2015;Doubravsky et al, 2020).…”

Section: M→s M (9)mentioning

confidence: 99%

Trend Modelling as a Support of Managerial Decision-Making Process in Relation to Customers

Schüller,

Doubravský,

Šimberová

2023

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The issue of managerial decision-making in relation to customers plays a significant role in any enterprise. Managerial decisions are often based on input information that is difficult to quantify. Therefore, common statistical methods cannot be easily used. A trend is the least informative quantifier. The goal of the paper is to present a new formal tool of the managerial decision-making process based on the trend model. This tool is demonstrated using the following variables: word of mouth, electronic word of mouth, brand trust, consumer experience and consumer price perception. These variables were chosen from scientific papers that confirmed a relationship among them. A trend analysis of the model involves assessing the relevant group of particular situations. The model’s predictions can be described as a sequence of these scenarios. The findings show that the trend model is a powerful and suitable tool in the managerial decision-making process.

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“…The linear qualitative reconstruction is the reverse process to the trend analysis given in (Dohnal and Doubravsky, 2015;Doubravsky and Dohnal, 2018). The trend analysis is the procedure to finding a solution of the trend model.…”

Section: Trend Reconstructionmentioning

confidence: 99%

“…For each scenario of Table 6 using the operations of qualitative sum and product, (see (Dohnal and Doubravsky, 2015)), the equations relevant to these scenarios can be identified.…”

Section: Trend Reconstructionmentioning

confidence: 99%

A Dynamic Knowledge Model of Project Time-Cost Analysis Based on Trend Modelling

Doubravský

Doskočil

Dohnal

2019

Period. Polytech. Soc. Man. Sci.

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This paper investigates the application of trend quantifiers of project time-cost analysis as a tool for decision-making support in the project management. Practical project management-related problems are solved under information shortages. It means that methods of statistical analysis cannot be easily used as they are based on the law of large numbers of observations. Numbers are information intensive quantifiers. The least information intensive quantifier is a trend; its values are increasing, constant, decreasing. If a derivative cannot be quantified by a trend, then nothing is known and therefore nothing can be analyzed/predicted. For this reason, the trend model M was created. The model M is based on a degraded set of differential equations or heuristics. A trend analysis of the model M is an evaluation of the relevant discrete set of solutions/scenarios S. A trend reconstruction is an evaluation of the model M if a (sub) set of scenarios S is given. The paper studies linear reconstruction, i.e. the model M is a set of linear differential equations. The trend reconstruction is partially reverse process to trend analysis. A case study has 7 variables (e.g. Project duration, Direct personnel costs, Indirect personal costs etc.) and the reconstructed set of linear differential equations has 7 equations. The set of 243 scenarios is obtained if this reconstructed set of trend linear equations is solved. Any future or past behavior of the model M can be described by a sequence of obtained scenarios.

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Qualitative Upper and Lower Approximations of Complex Nonlinear Chaotic and Nonchaotic Models

Cited by 4 publications

References 24 publications

Séries temporais e variáveis qualitativas

Séries temporais e variáveis qualitativas

Trend Modelling as a Support of Managerial Decision-Making Process in Relation to Customers

A Dynamic Knowledge Model of Project Time-Cost Analysis Based on Trend Modelling

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