referencias.bib

@ONLINE {ehlers,
    author = "Ehlers, R.S.",
    title  = "Análise de Séries Temporais",
    year   = "2009",
    url    = "http://www.icmc.usp.br/ ehlers/stemp/stemp.pdf."
}
@article{tyrpakovadeep,
    title={Deep Neural Networks for Sales Forecasting},
    year=2016,
    author={Tyrp{\'a}kov{\'a}, Nat{\'a}lia}
}

@article{zhang,
  title={Forecasting with artificial neural networks:: The state of the art},
  author={Zhang, Guoqiang and Patuwo, B Eddy and Hu, Michael Y},
  journal={International journal of forecasting},
  volume={14},
  number={1},
  pages={35--62},
  year={1998},
  publisher={Elsevier}
}

@article{russell1995modern,
  title={A modern approach},
  author={Russell, Stuart and Norvig, Peter and Intelligence, Artificial},
  journal={Artificial Intelligence. Prentice-Hall, Egnlewood Cliffs},
  volume={25},
  pages={27},
  year={1995}
}

@book{haykin2009,
  address = {Upper Saddle River, NJ},
  author = {Haykin, Simon S.},
  edition = {Third},
  keywords = {Book Learning NeuralNetwork},
  publisher = {Pearson Education},
  title = {Neural networks and learning machines},
  year = 2009
}

@book{deepLearning,
    title={Deep Learning},
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
    publisher={MIT Press},
    note={\url{http://www.deeplearningbook.org}},
    year={2016}
}

@book{morettin2006analise,
  title={An{\'a}lise de s{\'e}ries temporais},
  author={Morettin, Pedro A and Toloi, Cl{\'e}lia},
  year={2006},
  publisher={Blucher}
}

@article{giebel2011state,
  title={The state-of-the-art in short-term prediction of wind power: A
      literature overview},
  author={Giebel, Gregor and Brownsword, Richard and Kariniotakis, George and
      Denhard, Michael and Draxl, Caroline},
  journal={ANEMOS. plus},
  year={2011}
}

@book{wiley,
  title={Introduction to Time Series Analysis and Forecasting},
  author={Montgomery, D.C. and Jennings, C.L. and Kulahci, M.},
  isbn={9781118211502},
  series={Wiley Series in Probability and Statistics},
  url={https://books.google.com.br/books?id=-qaFi0oOPAYC},
  year={2011},
  publisher={Wiley}
}

@ARTICLE {co2data,
    author  = "C. D. Keeling and S. C. Piper and R. B. Bacastow and M. Wahlen
        and T. P. Whorf and M. Heimann and H. A. Meijer",
    title   = "Exchanges of atmospheric CO2 and 13CO2 with the terrestrial
        biosphere and oceans from 1978 to 2000. I. Global aspects",
    journal = "SIO Reference Series",
    year    = "2001",
    volume  = "No. 01-06",
    pages   = "88"
}

@book{box,
  title={Time series analysis: forecasting and control},
  author={Box, George EP and Jenkins, Gwilym M and Reinsel, Gregory C and
      Ljung, Greta M},
  year={2015},
  publisher={John Wiley \& Sons}
}

@book{machineLearning,
   title =     {Pattern Recognition and Machine Learning},
   author =    {Christopher M. Bishop},
   publisher = {Springer},
   isbn =      {9780387310732,0387310738},
   year =      {2006},
   series =    {Information science and statistics},
   edition =   {1st ed. 2006. Corr. 2nd printing},
   volume =    {},
}

@inproceedings{artigoClassificacao,
  title={Multi-column deep neural networks for image classification},
  author={Ciregan, Dan and Meier, Ueli and Schmidhuber, J{\"u}rgen},
  booktitle={Computer vision and pattern recognition (CVPR), 2012 IEEE
      conference on},
  pages={3642--3649},
  year={2012},
  organization={IEEE}
}

@book{haykin,
   title =     {Neural Networks. A Comprehensive Foundation},
   author =    {Simon Haykin},
   publisher = {Prentice Hall},
   isbn =      {9780132733502,0132733501},
   year =      {1998},
   series =    {},
   edition =   {2},
   volume =    {},
}

@article{regressao,
  title={Electric load forecasting using an artificial neural network},
  author={Park, Dong C and El-Sharkawi, MA and Marks, RJ and Atlas, LE and
      Damborg, MJ},
  journal={IEEE transactions on Power Systems},
  volume={6},
  number={2},
  pages={442--449},
  year={1991},
  publisher={IEEE}
}

@article{adam,
  author    = {Diederik P. Kingma and Jimmy Ba},
  title     = {Adam: {A} Method for Stochastic Optimization},
  journal   = {CoRR},
  volume    = {abs/1412.6980},
  year      = {2014},
  url       = {http://arxiv.org/abs/1412.6980},
  archivePrefix = {arXiv},
  eprint    = {1412.6980},
  timestamp = {Wed, 07 Jun 2017 14:40:52 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/KingmaB14},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{dickey,
 ISSN = {01621459},
 URL = {http://www.jstor.org/stable/2286348},
 author = {David A. Dickey and Wayne A. Fuller},
 journal = {Journal of the American Statistical Association},
 number = {366},
 pages = {427--431},
 publisher = {[American Statistical Association, Taylor and Francis, Ltd.]},
 title = {Distribution of the Estimators for Autoregressive Time Series With a
     Unit Root},
 volume = {74},
 year = {1979}
}

@book{ethem,
   title =     {Introduction to Machine Learning},
   author =    {Ethem Alpaydin},
   publisher = {The MIT Press},
   isbn =      {0262028182,9780262028189},
   year =      {2014},
   series =    {Adaptive Computation and Machine Learning series},
   edition =   {third edition},
   volume =    {}
}

@article{Lecun,
	doi = {10.1109/5.726791},
	url = {https://doi.org/10.1109%2F5.726791},
	year = 1998,
	publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
	volume = {86},
	number = {11},
	pages = {2278--2324},
	author = {Y. Lecun and L. Bottou and Y. Bengio and P. Haffner},
	title = {Gradient-based learning applied to document recognition},
	journal = {Proceedings of the {IEEE}}
}

@book{knight,
 author = {Rich, Elaine and Knight, Kevin},
 title = {Artificial Intelligence},
 year = {1990},
 isbn = {0070522634},
 edition = {2nd},
 publisher = {McGraw-Hill Higher Education},
}

@incollection{statiticalML,
title = "Chapter 9 - Statistical Estimation",
editor = "Masashi Sugiyama",
booktitle = "Introduction to Statistical Machine Learning",
publisher = "Morgan Kaufmann",
address = "Boston",
pages = "91 - 98",
year = "2016",
isbn = "978-0-12-802121-7",
doi = "https://doi.org/10.1016/B978-0-12-802121-7.00020-0",
url = "http://www.sciencedirect.com/science/article/pii/B9780128021217000200",
author = "Masashi Sugiyama",
keywords = "Point estimation, interval estimation, Parametric density estimation, Nonparametric density estimation, Regression, Classification, Model selection, Bootstrap confidence interval, Bayesian credible interval"
}

@book{timeseriesExample,
   title =     {Time Series Analysis and Forecasting by Example},
   author =    {Søren Bisgaard, Murat Kulahci},
   publisher = {Wiley},
   year =      {2011},
   series =    {Wiley Series in Probability and Statistics},
   edition =   {1}
}

@article{zhangbco,
title = "Stock market prediction of S\&P 500 via combination of improved BCO approach and BP neural network",
journal = "Expert Systems with Applications",
volume = "36",
number = "5",
pages = "8849 - 8854",
year = "2009",
issn = "0957-4174",
doi = "https://doi.org/10.1016/j.eswa.2008.11.028",
url = "http://www.sciencedirect.com/science/article/pii/S095741740800852X",
author = "Yudong Zhang and Lenan Wu",
keywords = "Bacterial chemotaxis optimization (BCO), Stock index prediction, Back propagation neural network (BPNN)",
abstract = "The paper proposed an improved bacterial chemotaxis optimization (IBCO), which is then integrated into the back propagation (BP) artificial neural network to develop an efficient forecasting model for prediction of various stock indices. Experiments show its better performance than other methods in learning ability and generalization."
}

@book{cryer,
  doi = {10.1007/978-0-387-75959-3},
  url = {https://doi.org/10.1007/978-0-387-75959-3},
  year = {2008},
  publisher = {Springer New York},
  author = {Jonathan D. Cryer and Kung-Sik Chan},
  title = {Time Series Analysis}
}

@inproceedings{vinay,
 author = {Gavirangaswamy, Vinay B. and Gupta, Gagan and Gupta, Ajay and Agrawal, Rajeev},
 title = {Assessment of ARIMA-based Prediction Techniques for Road-traffic Volume},
 booktitle = {Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems},
 series = {MEDES '13},
 year = {2013},
 isbn = {978-1-4503-2004-7},
 location = {Luxembourg, Luxembourg},
 pages = {246--251},
 numpages = {6},
 url = {http://doi.acm.org/10.1145/2536146.2536176},
 doi = {10.1145/2536146.2536176},
 acmid = {2536176},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {ARIMA, ARIMA-GARCH, GARCH, SARIMA, predictive analysis, traffic},
}

@inproceedings{reza,
  doi = {10.1109/rios.2016.7529496},
  url = {https://doi.org/10.1109/rios.2016.7529496},
  year = {2016},
  month = apr,
  publisher = {{IEEE}},
  author = {Soheila Mehrmolaei and Mohammad Reza Keyvanpour},
  title = {Time series forecasting using improved {ARIMA}},
  booktitle = {2016 Artificial Intelligence and Robotics ({IRANOPEN})}
}

@article{matias,
  title={Predicting taxi--passenger demand using streaming data},
  author={Moreira-Matias, Luis and Gama, Joao and Ferreira, Michel and Mendes-Moreira, Joao and Damas, Luis},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  volume={14},
  number={3},
  pages={1393--1402},
  year={2013},
  publisher={IEEE}
}


@Book{rob,
  author = { Hyndman, Rob J. and Athanasopoulos, George},
  title = { Forecasting : principles and practice },
  edition = { Print edition. },
  isbn = { 9780987507105 },
  publisher = { OTexts.com [Heathmont?, Victoria] },
  pages = { 291 pages ; },
  year = { 2014 },
  type = { Book },
  language = { English }
}

@article{eshel,
  title={The yule walker equations for the AR coefficients},
  author={Eshel, Gidon},
  journal={Internet resource},
  volume={2},
  pages={68--73},
  year={2003}
}

@phdthesis{silva_2018,
    place={Itajubá, MG},
    title={Análise comparativa entre os métodos auto-regressivo, integrado de médias móveis e rede neural artificial para previsão de séries temporais},
    author={Silva, Lara Moura Saúde},
    year={2018}
}

@article{jacobs_2014,
    title={Estudo Comparativo de Séries Temporais para Previsão de Vendas de Um Produto},
    volume={6},
    DOI={10.13084/2175-8018/ijie.v6n12p112-133},
    number={12},
    journal={Iberoamerican Journal of Industrial Engineering},
    author={Jacobs, W. and Zanini, R.r. and Costa, M.},
    year={2014},
    pages={112–133}
}

@article{ZHANG2003,
title = "Time series forecasting using a hybrid ARIMA and neural network model",
journal = "Neurocomputing",
volume = "50",
pages = "159 - 175",
year = "2003",
issn = "0925-2312",
doi = "https://doi.org/10.1016/S0925-2312(01)00702-0",
url = "http://www.sciencedirect.com/science/article/pii/S0925231201007020",
author = "G.Peter Zhang",
keywords = "ARIMA, Box–Jenkins methodology, Artificial neural networks, Time series forecasting",
abstract = "Autoregressive integrated moving average (ARIMA) is one of the popular linear models in time series forecasting during the past three decades. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. ARIMA models and ANNs are often compared with mixed conclusions in terms of the superiority in forecasting performance. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately."
}

@ARTICLE{zhang2017,
author={Z. {Zhao} and W. {Chen} and X. {Wu} and P. C. Y. {Chen} and J. {Liu}},
journal={IET Intelligent Transport Systems},
title={LSTM network: a deep learning approach for short-term traffic forecast}, 
year={2017},
volume={11},
number={2},
pages={68-75},
keywords={intelligent transportation systems;learning (artificial intelligence);recurrent neural nets;road traffic control;LSTM network;LSTM deep-learning approach;short-term traffic forecasting;intelligent transportation system;travel modes;travel routes;departure time;traffic management;traffic data analysis;computation power;long-short-term memory network;temporal-spatial correlation;two-dimensional network;memory units},
doi={10.1049/iet-its.2016.0208},
ISSN={1751-956X},
month={},}