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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

Analysis a Short-Term Time Series of Crop Sales Based on Machine Learning Methods

verfasst von : Mohammed A. Al-Gunaid, Maxim V. Shcherbakov, Vladislav N. Trubitsin, Alexandr M. Shumkin, Kirill Y. Dereguzov

Erschienen in: Creativity in Intelligent Technologies and Data Science

Verlag: Springer International Publishing

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Abstract

The main goal of this article is to solve the problem associated with identifying sales seasons in time series in order to build the most accurate forecast of sales of various crops and provide decision support and improve the efficiency of business processes of agro-industrial companies. In this regard, the necessity of developing an algorithm that allows to form a time series of sales in accordance with the seasons available in it to improve the accuracy of existing sales forecasting methods is justified. This study provides a detailed description of the problem and its solutions in the form of an algorithm, as well as a comparison of the accuracy of building prediction models before and after its application, which confirms the consistency of the developed method for the formation of time series.

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Vorheriges Kapitel Detection of the Patterns in the Daily Route Choices of the Urban Social Transport System Clients Based on the Decoupling of Passengers’ Preferences Between the Levels of Uncertainty
Nächstes Kapitel A Multifactor Small Business Management Model
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Metadaten
Titel
Analysis a Short-Term Time Series of Crop Sales Based on Machine Learning Methods
verfasst von
Mohammed A. Al-Gunaid
Maxim V. Shcherbakov
Vladislav N. Trubitsin
Alexandr M. Shumkin
Kirill Y. Dereguzov
Copyright-Jahr
2019
Buch
Creativity in Intelligent Technologies and Data Science

Print ISBN: 978-3-030-29742-8

Electronic ISBN: 978-3-030-29743-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-29743-5_15