映射领域的现状预测方法,跨行业的商业和经济特征

Simona Hašková, J. Kučera, Róbert Kuchár
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引用次数: 0

摘要

商业和经济特征预测的结果为利益相关者(企业所有者和管理者、投资者和股东)提供有价值的信息。本文的目的是提供在实践中应用的预测重要的商业和经济变量的方法的全面概述。这项研究是针对世界上大多数经济体选定的关键行业进行的。对过去十年发表的科学论文进行了广泛的文献回顾,发现最常用的预测方法包括人工神经网络、GARCH和ARIMA。这些方法足够强大,可以捕捉行业的细节,用于经济和商业预测目的。LS-SVM和ARIMA方法在较小程度上是分开使用的。其他方法的使用主要是为了验证其预测适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MAPPING CURRENT STATE IN THE FIELD OF PREDICTION METHODS OF BUSINESS AND ECONOMIC CHARACTERISTICS ACROSS INDUSTRIES
The results of the prediction of business and economic characteristics provide valuable information to stakeholders (business owners and managers, investors, and shareholders). The aim of the paper is to provide a comprehensive overview on methods applied in practice for predicting significant business and economic variables. The research is structured into selected key industries for most of the world's economies. An extensive literary review of the scientific papers published over the last decade revealed that the most used prediction methods include ANN, GARCH combined with ARIMA. These are the methods strong enough to capture the specifics of the industries for the economic and business prediction purposes. The LS-SVM and ARIMA methods are used separately to a lesser extent. The other methods were used mainly for the purpose of vali-dation of their predicting applicability.
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