Presentation 2022-01-18
Evaluation on Prediction Method for Missing Probability of Sensor Value based on Hierarchical Structure of Missing Value
Norifumi Hirata, Osamu Maeshima, Kiyohito Yoshihara,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Collecting sensor data via networks is important for IoT (Internet of Things) services.However, sensors sometimes have missing values by equipment failures, network failure, etc.It is possible to take actions against missing value by predicting a missing probability.For example, we can take a bid in an electricity power market except failed generators by predicting missing probability.In this paper, we propose and evaluate a prediction method for missing probability of sensor data hierarchical structure of missing value.As a result of the evaluation, the proposed method could reduce the prediction error of missing probability from 10.75% to 7.71% compared to a baseline method.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) time-series prediction / missing value / hierarchical structure / sensor value
Paper # IN2021-25
Date of Issue 2022-01-11 (IN)

Conference Information
Committee IN
Conference Date 2022/1/18(1days)
Place (in Japanese) (See Japanese page)
Place (in English) Online
Topics (in Japanese) (See Japanese page)
Topics (in English) Contents Distribution, Social Networking Services, Data Analytics and Processing Platform, Big data, etc.
Chair Kenji Ishida(Hiroshima City Univ.)
Vice Chair Kunio Hato(Internet Multifeed)
Secretary Kunio Hato(NTT)
Assistant

Paper Information
Registration To Technical Committee on Information Networks
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Evaluation on Prediction Method for Missing Probability of Sensor Value based on Hierarchical Structure of Missing Value
Sub Title (in English)
Keyword(1) time-series prediction
Keyword(2) missing value
Keyword(3) hierarchical structure
Keyword(4) sensor value
1st Author's Name Norifumi Hirata
1st Author's Affiliation KDDI Research, Inc(KDDI Research)
2nd Author's Name Osamu Maeshima
2nd Author's Affiliation KDDI Research, Inc(KDDI Research)
3rd Author's Name Kiyohito Yoshihara
3rd Author's Affiliation KDDI Research, Inc(KDDI Research)
Date 2022-01-18
Paper # IN2021-25
Volume (vol) vol.121
Number (no) IN-324
Page pp.pp.7-12(IN),
#Pages 6
Date of Issue 2022-01-11 (IN)