Fuzzy seasonality forecasting
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Cited by (59)
Developing seasonal z-number regression for waste-disposal forecasting in a Taiwanese hospital
2024, Applied Soft ComputingForecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors
2020, EnergyCitation Excerpt :Moreover, they verified that it is highly applicable to the prediction of seasonal time series. In addition, seasonal indexes were constructed to deal with seasonal fluctuations and the methods for prediction with the mixed model established by combining with other models are popular [46–48]. An et al. [49] adopted a signal filtering method based on empirical mode decomposition (EMD) and seasonal adjustment to process power demand series with seasonality.
Fuzzy regression analysis: Systematic review and bibliography
2019, Applied Soft Computing JournalCitation Excerpt :Watada [292, 293] proposes fuzzy time series models by using the concept of intersection of fuzzy numbers. Chang [294] discusses a fuzzy forecasting method for seasonality in time series data based on fuzzy regression models. Tseng et al. [67] use a fuzzy piecewise regression approach for the prediction of non-linear time series (see Section 4.1.5), and Tseng and Tzeng [295] combine seasonal ARIMA models with fuzzy regression models to develop fuzzy seasonal ARIMA models.
A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting
2015, International Journal of Production EconomicsCitation Excerpt :They also suggested from the results that the poor selection of parameter settings in BPNN model can lead to slow convergence and/or incorrect output. To tackle the uncertainty in seasonality, Chang (1997) presented a fuzzy forecasting technique for the seasonality in time series food sales. He analyzed both seasonal and trend fuzziness in his study.
Fully fuzzy polynomial regression with fuzzy neural networks
2014, NeurocomputingCitation Excerpt :Fuzzy regression analysis is an extension of the classical regression analysis in which some elements of the model are represented by fuzzy numbers. Fuzzy regression methods have been successfully applied to various problems such as forecasting [6,7,34,17,35] and engineering [15]. Thus, it is very important to develop numerical procedures that can appropriately treat fuzzy regression models.
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
2013, Information SciencesCitation Excerpt :A number of investigations have proposed different FR methods (e.g., see [3,5,7–9,17,18,38,41,47,64,75,76,84], among others). One that stands out is the study of Chang [6], who has proposed seasonal fuzzy regression. Here, fuzzy seasonality is defined by realizing the membership grades of the seasons to the fuzzy regression model.