Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/4612
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dc.contributor.authorChaudhuri, Narendra S.en_US
dc.date.accessioned2022-03-17T01:00:00Z-
dc.date.accessioned2022-03-17T15:34:58Z-
dc.date.available2022-03-17T01:00:00Z-
dc.date.available2022-03-17T15:34:58Z-
dc.date.issued2019-
dc.identifier.citationChakrabarti, P., Satpathy, B., Bane, S., Chakrabarti, T., Chaudhuri, N. S., & Siano, P. (2019). Business forecasting in the light of statistical approaches and machine learning classifiers doi:10.1007/978-981-13-9939-8_2en_US
dc.identifier.isbn9789811399381-
dc.identifier.issn1865-0929-
dc.identifier.otherEID(2-s2.0-85073905961)-
dc.identifier.urihttps://doi.org/10.1007/978-981-13-9939-8_2-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/4612-
dc.description.abstractThe paper focuses a non-conventional approach using Poisson and Binomial distributions for optimum strategic business forecasting. An analysis has been carried out based on profit-loss statistics of consecutive ten years. Relevance of Poisson distribution in business forecasting is shown. Relevance of Binomial distribution in business forecasting is also shown. Curve fitting has been applied to reveal further some discovered facts related to gain analysis. Linear Regression, Exponential, Parabolic, Power function, Logarithmic, polynomial of degree 2 and 4 curves are shown as cases. Novel facts related to business forecasting in the light of machine learning classifiers have been pointed out leading to new directions in the field of research in business analytics. © Springer Nature Singapore Pte Ltd 2019.en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.sourceCommunications in Computer and Information Scienceen_US
dc.subjectCurve fittingen_US
dc.subjectLearning systemsen_US
dc.subjectMachine learningen_US
dc.subjectPoisson distributionen_US
dc.subjectBinomial distributionen_US
dc.subjectBusiness analyticsen_US
dc.subjectBusiness forecastingen_US
dc.subjectConventional approachen_US
dc.subjectCurve-fiten_US
dc.subjectPower functionsen_US
dc.subjectStatistical approachen_US
dc.subjectStrategic businessen_US
dc.subjectForecastingen_US
dc.titleBusiness forecasting in the light of statistical approaches and machine learning classifiersen_US
dc.typeConference Paperen_US
Appears in Collections:Department of Computer Science and Engineering

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