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• W4387363602 abstract "In the study of time series forecasting based on fuzzy cognitive maps (FCMs), the causalities between past values and future values are represented by real-valued weights in [-1,1]. However, for interval-valued time series (ITS), the causalities are affected by various uncertainties including ways of measuring and ways of intervals influencing intervals and thus involve uncertainty. Therefore, real-valued weights are no longer enough for characterizing such causalities, equipping FCMs with interval-valued weights becomes necessary and resulting in interval cognitive maps (ICMs). In this case, how to determine the interval-valued weights of an ICM becomes a crucial problem. To solve this problem, this paper first proposes the principle of justifiable granularity for interval-valued data, which is guaranteed to accumulate enough experimental evidence and effectively express the ITS, then develops a reasonable method that can optimally determine the interval-valued weights and enable the interval-valued weights having clear semantics. By means of the proposed method for determining interval-valued weights, an ICM-based ITS forecasting model is established, which can not only deal with the uncertainty of causalities between interval-valued data, but also avoid counterintuitive outputs which often appeared in existing ITS forecasting models. Experimental results show the good performance of the proposed forecasting model." @default.
• W4387363602 created "2023-10-06" @default.
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• W4387363602 date "2024-01-01" @default.
• W4387363602 modified "2023-10-12" @default.
• W4387363602 title "Build Interval-Valued Time Series Forecasting Model with Interval Cognitive Map Trained by Principle of Justifiable Granularity" @default.
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• W4387363602 doi "https://doi.org/10.1016/j.ins.2023.119756" @default.
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