A fuzzy inference system modeling approach for interval-valued symbolic data forecasting
| Autor: | Ballini, Rosangela, 1969 |
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| Přispěvatelé: | UNIVERSIDADE ESTADUAL DE CAMPINAS |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Repositório Institucional da Unicamp Universidade Estadual de Campinas (UNICAMP) instacron:UNICAMP Repositório da Produção Científica e Intelectual da Unicamp |
| Popis: | Agradecimentos: The authors thank the Brazilian Ministry of Education (CAPES) and the São Paulo Research Foundation (FAPESP) for their support Abstract: This paper suggests a fuzzy inference system (iFIS) modeling approach for interval-valued time series forecasting. Interval-valued data arise quite naturally in many situations in which such data represent uncertainty/variability or when comprehensive ways to summarize large data sets are required. The method comprises a fuzzy rule-based framework with affine consequents which provides a (non)linear framework that processes interval-valued symbolic data. The iFIS antecedents identification uses a fuzzy c-means clustering algorithm for interval-valued data with adaptive distances, whereas parameters of the linear consequents are estimated with a center-range methodology to fit a linear regression model to symbolic interval data. iFIS forecasting power, measured by accuracy metrics and statistical tests, was evaluated through Monte Carlo experiments using both synthetic interval-valued time series with linear and chaotic dynamics, and real financial interval-valued time series. The results indicate a superior performance of iFIS compared to traditional alternative single-valued and interval-valued forecasting models by reducing 19% on average the predicting errors, indicating that the suggested approach can be considered as a promising tool for interval time series forecasting FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPES Fechado |
| Databáze: | OpenAIRE |
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