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This paper proposes a methodology to create an interpretable fuzzy model for monthly rainfall time series prediction. The proposed methodology incorporates the advantages of artificial neural network, fuzzy logic and genetic algorithm. In the first step, the differences between the time series data are calculated and they are used to define the interval between the membership functions of a Mamdani-type...
This study investigates the data preparation process for predictive modelling of the evolution of complex networked systems, using an e -- mail based social network as an example. In particular, we focus on the selection of optimal time window size for building a time series of network snapshots, which forms the input of chosen predictive models. We formulate this issue as a constrained multi -- objective...
Accuracy of forecasting in fuzzy based prediction system considerably depends on subjectively decided parameters such as fuzzy membership function. In this paper, we presented a novice concept to optimize Ordered Weight Aggregation (OWA) based forecasting model by Genetic Algorithm. Firstly, OWA weights are determined on the basis of importance of fuzzy set in the system by employing regularly increasing...
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