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• W3175239188 abstract "As one type of time series, interval-valued time series (ITS) is encountered frequently in many fields such as finance, environment, agriculture and so on since it can describe the uncertainty and variability of observed variables. The modeling of ITS is an ongoing issue pursued by researchers. In this paper, a novel rule-based granular ITS model is proposed by considering the interval-valued data as interval information granules in the framework of granular computing (GrC). The development of the proposed granular model consists of three components with progressive relationship, that is, the generation of granular prototypes, the formation of initial granular model and the refinement of the initial granular model. Further, the detail computation of reasoning of the corresponding granular model is also given. The resulting granular models have not only interpretability but also an ability to process ITS containing linguistic variables. Numerical experiments on six financial datasets and two meteorological datasets from real world reveal the impact of the parameter involved in the proposed method on the resulting granular model, and also exhibit that the resulting granular model is superior to seven existing competitive models including interval Holt one, multi-output support vector regression one and interval arithmetic-based multilayer perceptron one etc." @default.
• W3175239188 created "2021-07-05" @default.
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• W3175239188 date "2021-09-01" @default.
• W3175239188 modified "2023-10-16" @default.
• W3175239188 title "A rule-based granular model development for interval-valued time series" @default.
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• W3175239188 doi "https://doi.org/10.1016/j.ijar.2021.06.009" @default.
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