英文标题：
《Curriculum Learning in Deep Neural Networks for Financial Forecasting》
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作者：
Allison Koenecke and Amita Gajewar
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最新提交年份：
2019
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英文摘要：
  For any financial organization, computing accurate quarterly forecasts for various products is one of the most critical operations. As the granularity at which forecasts are needed increases, traditional statistical time series models may not scale well. We apply deep neural networks in the forecasting domain by experimenting with techniques from Natural Language Processing (Encoder-Decoder LSTMs) and Computer Vision (Dilated CNNs), as well as incorporating transfer learning. A novel contribution of this paper is the application of curriculum learning to neural network models built for time series forecasting. We illustrate the performance of our models using Microsoft\'s revenue data corresponding to Enterprise, and Small, Medium & Corporate products, spanning approximately 60 regions across the globe for 8 different business segments, and totaling in the order of tens of billions of USD. We compare our models\' performance to the ensemble model of traditional statistics and machine learning techniques currently used by Microsoft Finance. With this in-production model as a baseline, our experiments yield an approximately 30% improvement in overall accuracy on test data. We find that our curriculum learning LSTM-based model performs best, showing that it is reasonable to implement our proposed methods without overfitting on medium-sized data.
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中文摘要：
对于任何金融组织来说，计算各种产品的准确季度预测是最关键的操作之一。随着所需预测粒度的增加，传统的统计时间序列模型可能无法很好地扩展。我们通过实验自然语言处理（编码器-解码器LSTM）和计算机视觉（扩展CNN）技术，以及结合迁移学习，将深度神经网络应用于预测领域。本文的一个新贡献是将课程学习应用于为时间序列预测构建的神经网络模型。我们使用微软的收入数据来说明我们的模型的性能，这些数据对应于企业、中小型企业和公司产品，覆盖全球约60个地区，涉及8个不同的业务部门，总额约为数百亿美元。我们将我们的模型的性能与Microsoft Finance目前使用的传统统计和机器学习技术的集成模型进行比较。以这一生产中模型为基准，我们的实验使测试数据的总体准确性提高了约30%。我们发现，基于LSTM的课程学习模型表现最好，表明在不过度拟合中等规模数据的情况下实施我们提出的方法是合理的。
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分类信息：

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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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：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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