paper.bib

@ARTICLE{2017AGU,
    author = {{Camporeale}, E. and {Chandorkar}, M.},
    title = "{Bayesian inference of radiation belt loss timescales.}",
    journal = {AGU Fall Meeting Abstracts},
    keywords = {1942 Machine learning, INFORMATICS, 1986 Statistical methods: Inferential, INFORMATICS, 2799 General or miscellaneous, MAGNETOSPHERIC PHYSICS, 7924 Forecasting, SPACE WEATHER},
    year = 2017,
    month = dec,
    adsurl = {http://adsabs.harvard.edu/abs/2017AGUFMSM23A2583C},
    adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}


@ARTICLE{GPDst,
    author={M. Chandorkar and E. Camporeale and S. Wing},
    journal={Space Weather},
    title={Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach},
    year={2017},
    volume={15},
    number={8},
    pages={1004-1019},
    abstract={We present a methodology for generating probabilistic predictions for the Disturbance Storm Time (Dst) geomagnetic activity index. We focus on the One Step Ahead prediction task and use the OMNI hourly resolution data to build our models. Our proposed methodology is based on the technique of Gaussian Process Regression. Within this framework we develop two models; Gaussian Process Autoregressive (GP-AR) and Gaussian Process Autoregressive with eXogenous inputs (GP-ARX). We also propose a criterion to aid model selection with respect to the order of autoregressive inputs. Finally, we test the performance of the GP-AR and GP-ARX models on a set of 63 geomagnetic storms between 1998 and 2006 and illustrate sample predictions with error bars for some of these events.},
    keywords={Autoregressive processes;Forecasting;Gaussian processes;Indexes;Mathematical model;Meteorology;Predictive models},
    doi={10.1002/2017SW001627},
    month={Aug}
}

@incollection{CHANDORKAR2018237,
    title = "Chapter 9 - Probabilistic Forecasting of Geomagnetic Indices Using Gaussian Process Models",
    editor = "Enrico Camporeale and Simon Wing and Jay R. Johnson",
    booktitle = "Machine Learning Techniques for Space Weather",
    publisher = "Elsevier",
    pages = "237 - 258",
    year = "2018",
    isbn = "978-0-12-811788-0",
    doi = "https://doi.org/10.1016/B978-0-12-811788-0.00009-3",
    url = "http://www.sciencedirect.com/science/article/pii/B9780128117880000093",
    author = "Mandar Chandorkar and Enrico Camporeale",
    keywords = "Gaussian process, Space weather, Probabilistic forecasting, Geomagnetic indices",
    abstract = "In this chapter, we give the reader an in-depth view into building of probabilistic forecasting models for geomagnetic time series using the Gaussian process methodology outlined in the previous chapters. We highlight design decisions and practical issues that must be addressed in order to use Gaussian process models for probabilistic prediction of a quantity of interest. As a pedagogical example, we formulate, train, and test a family of Gaussian process auto-regressive models for 1-h ahead prediction of the Dst geomagnetic index."
}

@misc{tensorflow2015-whitepaper,
    title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
    url={https://www.tensorflow.org/},
    note={Software available from tensorflow.org},
    author={
    Mart\'{\i}n~Abadi and
    Ashish~Agarwal and
    Paul~Barham and
    Eugene~Brevdo and
    Zhifeng~Chen and
    Craig~Citro and
    Greg~S.~Corrado and
    Andy~Davis and
    Jeffrey~Dean and
    Matthieu~Devin and
    Sanjay~Ghemawat and
    Ian~Goodfellow and
    Andrew~Harp and
    Geoffrey~Irving and
    Michael~Isard and
    Yangqing Jia and
    Rafal~Jozefowicz and
    Lukasz~Kaiser and
    Manjunath~Kudlur and
    Josh~Levenberg and
    Dandelion~Man\'{e} and
    Rajat~Monga and
    Sherry~Moore and
    Derek~Murray and
    Chris~Olah and
    Mike~Schuster and
    Jonathon~Shlens and
    Benoit~Steiner and
    Ilya~Sutskever and
    Kunal~Talwar and
    Paul~Tucker and
    Vincent~Vanhoucke and
    Vijay~Vasudevan and
    Fernanda~Vi\'{e}gas and
    Oriol~Vinyals and
    Pete~Warden and
    Martin~Wattenberg and
    Martin~Wicke and
    Yuan~Yu and
    Xiaoqiang~Zheng},
    year={2015},
}

@Misc{tfscala,
    author =   {Emmanuel Antonios Platanios},
    title =    {{Tensorflow Scala}: Scala API for Tensorflow},
    howpublished = {\url{http://platanios.org/tensorflow_scala/}},
    year = {2017}
}

@Misc{renjin,
    author =   {BeDataDriven B.V.},
    title =    {{Renjin}: JVM-based interpreter for the R language for the statistical analysis.},
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}

@Misc{scala,
    author =   {Scala Center, École Polytechnique Fédérale Lausanne (EPFL).},
    title =    {{Scala}: The Scala Programming Language.},
    howpublished = {\url{https://www.scala-lang.org}},
    year = {2002}
}