Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/4612
Title: Business forecasting in the light of statistical approaches and machine learning classifiers
Authors: Chaudhuri, Narendra S.
Keywords: Curve fitting;Learning systems;Machine learning;Poisson distribution;Binomial distribution;Business analytics;Business forecasting;Conventional approach;Curve-fit;Power functions;Statistical approach;Strategic business;Forecasting
Issue Date: 2019
Publisher: Springer Verlag
Citation: Chakrabarti, 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_2
Abstract: The 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.
URI: https://doi.org/10.1007/978-981-13-9939-8_2
https://dspace.iiti.ac.in/handle/123456789/4612
ISBN: 9789811399381
ISSN: 1865-0929
Type of Material: Conference Paper
Appears in Collections:Department of Computer Science and Engineering

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