Dynamically Forming a Group of Human Forecasters and Machine Forecaster for Forecasting Economic Indicators
Dynamically Forming a Group of Human Forecasters and Machine Forecaster for Forecasting Economic Indicators
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DOI：
10.24963/ijcai.2018/64
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
T. Miyoshi;S. Matsubara
中科院分区：
文献类型：
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作者：
T. Miyoshi;S. Matsubara
在通货膨胀预测任务中，如何将人工预测和机器预测结合起来？基于机器学习的预测器根据从过去的时间序列数据构建的统计模型进行预测，而人类则考虑经济政策等各种信息。研究了不同预报的组合方法，如集合和一致性方法。然而，这些方法总是使用相同的组合方式，而不管情况（输入）如何，这使得难以利用不同类型预测器的优点。为了克服这个缺点，我们提出了一种集成方法，用于估计机器预测的预期误差，并动态确定集成中包含的最佳人类数量。我们使用美国通货膨胀的七个数据集对所提出的方法进行了评估，并证实它在四个数据集上达到了最高的预测精度，在两个数据集上达到了传统方法的最高精度。
How can human forecasts and a machine forecast be combined in inflation forecast tasks? A machine-learning-based forecaster makes a forecast based on a statistical model constructed from past time-series data, while humans take varied information such as economic policies into account. Combination methods for different forecasts have been studied such as ensemble and consensus methods. These methods, however, always use the same manner of combination regardless of the situation (input), which makes it difficult to use the advantages of different types of forecasters. To overcome this drawback, we propose an ensemble method for estimating the expected error of a machine forecast and dynamically determining the optimal number of humans included in the ensemble. We evaluated the proposed method by using the seven datasets on U.S. inflation and confirmed that it attained the highest forecast accuracy for four datasets and the same accuracy as the highest one of traditional methods for two datasets.