Dynamic pre-training of Deep Recurrent Neural Networks for predicting environmental monitoring data

Bun Theang Ong, Komei Sugiura, Koji Zettsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

In this paper, we introduce a Deep Recurrent Neural Network (DRNN) that is trained using a novel autoencoder pre-training method especially designed for the task of time series prediction. Our main objective is to perform predictions of environmental monitoring data using open sensors with improved accuracy over the currently employed methods. The numerical experiments show that our proposed pre-training method is superior that a canonical and a state-of-the-art auto-encoder training method when applied to time series prediction. On the specific case of fine particulate matter (PM2.5) forecasting in Japan, the experiments confirm that when compared against the PM2.5 prediction system VENUS employed by the Japanese Government, our technique improves the accuracy of PM2.5 concentration level predictions that are being reported in Japan.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
EditorsWo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages760-765
Number of pages6
ISBN (Electronic)9781479956654
DOIs
Publication statusPublished - 2015 Jan 7
Event2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States
Duration: 2014 Oct 272014 Oct 30

Publication series

NameProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Other

Other2nd IEEE International Conference on Big Data, IEEE Big Data 2014
CountryUnited States
CityWashington
Period14/10/2714/10/30

Keywords

  • Deep Learning
  • Environmental Sensor Data
  • Fine Particulate Matter
  • Pre-training
  • Recurrent Neural Networks
  • Time Series Prediction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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  • Cite this

    Ong, B. T., Sugiura, K., & Zettsu, K. (2015). Dynamic pre-training of Deep Recurrent Neural Networks for predicting environmental monitoring data. In W. Chang, J. Huan, N. Cercone, S. Pyne, V. Honavar, J. Lin, X. T. Hu, C. Aggarwal, B. Mobasher, J. Pei, & R. Nambiar (Eds.), Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014 (pp. 760-765). [7004302] (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2014.7004302