使用选定的方法识别具有趋势和季节性的销售系列

Anna Borucka, Jolanta Wierzbicka
{"title":"使用选定的方法识别具有趋势和季节性的销售系列","authors":"Anna Borucka, Jolanta Wierzbicka","doi":"10.5604/01.3001.0053.9699","DOIUrl":null,"url":null,"abstract":"The actions taken by companies today are increasingly precise, dedicated to specific market expectations therefore require an increasingly solid basis for decision-making, especially at the strategic level. A key support in this area is forecasting methods that allow probing about the future with a certain probability. Therefore, methods for determining such forecasts are developing rapidly, creating an arsenal of usable tools. However, many of them rely on modern information retrieval systems, require accurate data with many variables and a long history. Despite the advancing digitization, this is still unattainable for many (especially small) businesses. Therefore, the presentation and methods of short-term forecasting using analytical models should not be abandoned, because for many companies this is the best solution, and often the only one possible. This idea became the genesis of this article. It selects and presents methods dedicated to time series characterized by trend and seasonality, which are quite difficult to identify. They were compared and a method of model selection strategy was presented to select the forecast with the highest reliability. The models presented are easy to interpret and utilitarian and can provide effective support for supply chain management processes.","PeriodicalId":470699,"journal":{"name":"International Journal of New Economics and Social Sciences","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"IDENTIFICATION OF SALES SERIES WITH TREND AND SEASONALITY USING SELECTED METHODS\",\"authors\":\"Anna Borucka, Jolanta Wierzbicka\",\"doi\":\"10.5604/01.3001.0053.9699\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The actions taken by companies today are increasingly precise, dedicated to specific market expectations therefore require an increasingly solid basis for decision-making, especially at the strategic level. A key support in this area is forecasting methods that allow probing about the future with a certain probability. Therefore, methods for determining such forecasts are developing rapidly, creating an arsenal of usable tools. However, many of them rely on modern information retrieval systems, require accurate data with many variables and a long history. Despite the advancing digitization, this is still unattainable for many (especially small) businesses. Therefore, the presentation and methods of short-term forecasting using analytical models should not be abandoned, because for many companies this is the best solution, and often the only one possible. This idea became the genesis of this article. It selects and presents methods dedicated to time series characterized by trend and seasonality, which are quite difficult to identify. They were compared and a method of model selection strategy was presented to select the forecast with the highest reliability. The models presented are easy to interpret and utilitarian and can provide effective support for supply chain management processes.\",\"PeriodicalId\":470699,\"journal\":{\"name\":\"International Journal of New Economics and Social Sciences\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of New Economics and Social Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5604/01.3001.0053.9699\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of New Economics and Social Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5604/01.3001.0053.9699","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

今天,公司采取的行动越来越精确,致力于特定的市场预期,因此需要越来越坚实的决策基础,特别是在战略层面。在这个领域的一个关键支持是预测方法,它允许以一定的概率探测未来。因此,确定这种预测的方法正在迅速发展,创造了一个可用工具库。然而,它们中的许多依赖于现代信息检索系统,需要精确的数据,变量多,历史悠久。尽管数字化在不断发展,但对于许多(尤其是小型)企业来说,这仍然是无法实现的。因此,不应该放弃使用分析模型进行短期预测的表示和方法,因为对许多公司来说,这是最好的解决方案,而且往往是唯一可能的解决方案。这个想法成为这篇文章的起源。它选择并提出了专门用于具有趋势和季节性特征的时间序列的方法,这些方法很难识别。在此基础上,提出了一种模型选择策略,选择可靠性最高的预测模型。所提出的模型易于解释和实用,可以为供应链管理过程提供有效的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IDENTIFICATION OF SALES SERIES WITH TREND AND SEASONALITY USING SELECTED METHODS
The actions taken by companies today are increasingly precise, dedicated to specific market expectations therefore require an increasingly solid basis for decision-making, especially at the strategic level. A key support in this area is forecasting methods that allow probing about the future with a certain probability. Therefore, methods for determining such forecasts are developing rapidly, creating an arsenal of usable tools. However, many of them rely on modern information retrieval systems, require accurate data with many variables and a long history. Despite the advancing digitization, this is still unattainable for many (especially small) businesses. Therefore, the presentation and methods of short-term forecasting using analytical models should not be abandoned, because for many companies this is the best solution, and often the only one possible. This idea became the genesis of this article. It selects and presents methods dedicated to time series characterized by trend and seasonality, which are quite difficult to identify. They were compared and a method of model selection strategy was presented to select the forecast with the highest reliability. The models presented are easy to interpret and utilitarian and can provide effective support for supply chain management processes.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信