論文

査読有り 筆頭著者 本文へのリンクあり
2018年7月

Dynamically Forming a Group of Human Forecasters and Machine Forecaster for Forecasting Economic Indicators

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
  • Takahiro Miyoshi
  • ,
  • Shigeo Matsubara

開始ページ
461
終了ページ
467
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.24963/ijcai.2018/64
出版者・発行元
International Joint Conferences on Artificial Intelligence Organization

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.

リンク情報
DOI
https://doi.org/10.24963/ijcai.2018/64 本文へのリンクあり
ID情報
  • DOI : 10.24963/ijcai.2018/64

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