Development of an Abnormality Predictor Detection Method Using Partial Observation Markov Decision Process (POMDP)

DOI

Bibliographic Information

Other Title
  • 部分観測マルコフ決定過程(POMDP)を用いた異常前兆予測検知手法の開発

Description

<p>Time series anomaly detection methods are applied for various fields. These methods basically assume distributions for data and users need to set threshold to detect anomality. Otherwise in reinforcement learning, an agent can learn desirable action through interaction with environment and the agent don’t need to know environment. By applying reinforcement learning for anomaly detection, it is possible to detect anomality from trial and error without assumptions. In this paper to deal with time series, we performed anomaly detection using Partially Observable MDP(POMDP). Furthermore, we compared accuracy by changing LSTM steps.</p>

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Keywords

Details 詳細情報について

  • CRID
    1390285300166015872
  • NII Article ID
    130007856891
  • DOI
    10.11517/pjsai.jsai2020.0_2i4gs201
  • ISSN
    27587347
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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