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• W4226028208 endingPage "104844" @default.
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• W4226028208 abstract "In fuzzy logical relationship (FLR) based forecasting models, FLRs play a vital role. In each FLR of such models, the engaged observations in premise and consequent are consecutive at time. The association (named short association in this paper) implied by the premise and consequent of such an FLR can reflect some of the properties and regularities hidden in a time series, which has been verified by more successful applications. However, there exists other kind of associations that cannot be described by short association. In this paper, the concept of long association to describe these different associations is proposed. And long-association FLRs are then proposed to reflect such long associations. For a long-association FLR, the engaged observations in the premise and consequent are not consecutive at time. This new kind of FLRs exists more and can overcome one deficiency of the existing FLR based forecasting models: no FLR available for forecasting occurred often at some prediction moments. Departure from long-association FLRs, we construct trend long-association FLRs to reflect the trend long associations in time series and design a novel forecasting model. The advantages of the proposed (trend) long-association FLRs and the superiority of the proposed model are verified in experiments with comparisons with other forecasting models." @default.
• W4226028208 created "2022-05-05" @default.
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• W4226028208 date "2022-06-01" @default.
• W4226028208 modified "2023-10-16" @default.
• W4226028208 title "Incorporate long association into high-order fuzzy logical relationship based time series forecasting" @default.
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• W4226028208 doi "https://doi.org/10.1016/j.engappai.2022.104844" @default.
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