A Hypothesis Discovery Method for Predicting Change in Multidimensional Time-series Data

研究成果: Conference contribution

抄録

With the development of IoT technology, it has become possible to accumulate and regularly measure multidimensional time-series data. In this study, we focus on the usage of multidimensional time-series data from printer products' log data and propose a method for its analysis. In addition to the number of sheets printed by each customer, the log data includes various time-series information such as the amount of remaining toner, the number of stoppages that occur, and the activation times. To utilize these data for business purposes, it is desirable to construct a model for predicting future changes in use characteristics for each customer. In this study, we apply the random forest algorithm to predict such changes. However, if all measurable features of the problem are included, the model becomes complex and cannot be interpreted. Although the accuracy is relatively high if an appropriate learning algorithm is applied, the complex model tends to overfit the training data. In this paper, we propose a method to select the modeling features that can be interpreted by graph mining while maintaining accuracy. This would enable us to interpret the data at the field level and discover the hypotheses that are necessary for planned marketing policies. Finally, the proposed method is applied to real data and its efficacy is demonstrated.

本文言語English
ホスト出版物のタイトル2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ854-859
ページ数6
2020-October
ISBN(電子版)9781728185262
DOI
出版ステータスPublished - 2020 10 11
イベント2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada
継続期間: 2020 10 112020 10 14

Conference

Conference2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
国/地域Canada
CityToronto
Period20/10/1120/10/14

ASJC Scopus subject areas

  • ソフトウェア
  • 制御およびシステム工学
  • 人間とコンピュータの相互作用
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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