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摘要翻译：
商品的需求函数通常是周期性的，具有趋势性或随机性等特征。文献中大多数现有的需求预测技术都是为了管理和预测这种类型的需求功能而设计的。然而，如果需求函数本质上是块状的，那么一般的需求预测技术可能会失败，因为该函数具有不寻常的特征。正确识别潜在的需求函数和使用最合适的预测技术变得至关重要。在本文中，我们将试图探索不同类型的需求函数的关键特征，并将它们与已知的统计分布联系起来。通过对过去实际需求数据的统计分布进行拟合，我们可以识别出正确的需求函数，从而可以应用最合适的预测技术来获得改进的预测结果。我们将该方法应用于一个实际案例研究，以显示所获得的预测误差的减少。
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英文标题：
《Identification of Demand through Statistical Distribution Modeling for
Improved Demand Forecasting》
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作者：
Murphy Choy and Michelle L.F. Cheong
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最新提交年份：
2011
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分类信息：
一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Quantitative Finance 数量金融学
二级分类：General Finance 一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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英文摘要：
Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions. However, if the demand function is lumpy in nature, then the general demand forecasting techniques may fail given the unusual characteristics of the function. Proper identification of the underlying demand function and using the most appropriate forecasting technique becomes critical. In this paper, we will attempt to explore the key characteristics of the different types of demand function and relate them to known statistical distributions. By fitting statistical distributions to actual past demand data, we are then able to identify the correct demand functions, so that the the most appropriate forecasting technique can be applied to obtain improved forecasting results. We applied the methodology to a real case study to show the reduction in forecasting errors obtained.
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PDF链接：
https://arxiv.org/pdf/1110.0062
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