@inproceedings{b787749283cf44f0a23353edf15fe53c,
title = "Dynamic pre-training of Deep Recurrent Neural Networks for predicting environmental monitoring data",
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.",
keywords = "Deep Learning, Environmental Sensor Data, Fine Particulate Matter, Pre-training, Recurrent Neural Networks, Time Series Prediction",
author = "Ong, {Bun Theang} and Komei Sugiura and Koji Zettsu",
year = "2015",
month = jan,
day = "7",
doi = "10.1109/BigData.2014.7004302",
language = "English",
series = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "760--765",
editor = "Wo Chang and Jun Huan and Nick Cercone and Saumyadipta Pyne and Vasant Honavar and Jimmy Lin and Hu, {Xiaohua Tony} and Charu Aggarwal and Bamshad Mobasher and Jian Pei and Raghunath Nambiar",
booktitle = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",
note = "2nd IEEE International Conference on Big Data, IEEE Big Data 2014 ; Conference date: 27-10-2014 Through 30-10-2014",
}