英文标题：
《Revenue Forecasting for Enterprise Products》
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作者：
Amita Gajewar, Gagan Bansal
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最新提交年份：
2016
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英文摘要：
  For any business, planning is a continuous process, and typically business-owners focus on making both long-term planning aligned with a particular strategy as well as short-term planning that accommodates the dynamic market situations. An ability to perform an accurate financial forecast is crucial for effective planning. In this paper, we focus on providing an intelligent and efficient solution that will help in forecasting revenue using machine learning algorithms. We experiment with three different revenue forecasting models, and here we provide detailed insights into the methodology and their relative performance measured on real finance data. As a real-world application of our models, we partner with Microsoft\'s Finance organization (department that reports Microsoft\'s finances) to provide them a guidance on the projected revenue for upcoming quarters.
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中文摘要：
对于任何企业来说，规划都是一个持续的过程，通常企业主都会将重点放在使长期规划与特定战略保持一致，以及使短期规划适应动态的市场形势。执行准确财务预测的能力对于有效规划至关重要。在本文中，我们致力于提供一种智能高效的解决方案，该解决方案将有助于使用机器学习算法预测收入。我们对三种不同的收入预测模型进行了实验，在此我们提供了对该方法及其在实际财务数据上衡量的相对绩效的详细见解。作为我们模型的真实应用程序，我们与Microsoft的财务组织（报告Microsoft财务的部门）合作，为他们提供关于未来季度预计收入的指导。
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分类信息：

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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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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