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dc.contributor.authorMajhi, Ritanjali-
dc.contributor.authorPanda, Ganapati-
dc.contributor.authorMajhi, Babita-
dc.contributor.authorPanigrahi, S. K.-
dc.contributor.authorMishra, Manoj Ku.-
dc.date.accessioned2024-11-22T06:04:05Z-
dc.date.available2024-11-22T06:04:05Z-
dc.date.issued2009-
dc.identifier.citation10.1109/NABIC.2009.5393740en_US
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/1687-
dc.descriptionNITWen_US
dc.description.abstractThe paper aims to develop an efficient forecasting model using differential evolution (DE) based learning rule. The structure chosen is an adaptive linear combiner whose weights are trained using DE. The prediction performance of the resulting model is evaluated by feeding features of retail sales data for different months’ ahead prediction. These results are compared with those obtained by GA based approach. The comparison demonstrates improved prediction of sales data by the proposed DE methoden_US
dc.language.isoenen_US
dc.publisher2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedingsen_US
dc.subjectSales forecastingen_US
dc.subjectDifferntial evolutionen_US
dc.titleForecasting of Retail Sales Data Using Differential Evolutionen_US
dc.typeOtheren_US
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