5. Bibliografía#
Maurice S Bartlett. On the theoretical specification and sampling properties of autocorrelated time-series. Supplement to the Journal of the Royal Statistical Society, 8(1):27–41, 1946.
John W Tukey and others. Exploratory data analysis. Volume 2. Reading, MA, 1977.
David A Dickey and Wayne A Fuller. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a):427–431, 1979.
Robert B Cleveland, William S Cleveland, Jean E McRae, Irma Terpenning, and others. Stl: a seasonal-trend decomposition. J. Off. Stat, 6(1):3–73, 1990.
George EP Box and David A Pierce. Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American statistical Association, 65(332):1509–1526, 1970.
Greta M Ljung and George EP Box. On a measure of lack of fit in time series models. Biometrika, 65(2):297–303, 1978.
Hirotugu Akaike. A new look at the statistical model identification. IEEE transactions on automatic control, 19(6):716–723, 1974.
Gideon Schwarz. Estimating the dimension of a model. The annals of statistics, pages 461–464, 1978.
Clifford M Hurvich and Chih-Ling Tsai. Regression and time series model selection in small samples. Biometrika, 76(2):297–307, 1989.
D.C. Montgomery, L.A. Johnson, and J.S. Gardiner. Forecasting and Time Series Analysis. Industrial engineering series. McGraw-Hill, 1990. ISBN 9780070428584. URL: https://books.google.com.co/books?id=t9HuAAAAMAAJ.
Robert Goodell Brown. Smoothing, forecasting and prediction of discrete time series. Courier Corporation, 2004.
B. Abraham and J. Ledolter. Statistical Methods for Forecasting. Wiley Series in Probability and Statistics. Wiley, 2009. ISBN 9780470317297. URL: https://books.google.com.co/books?id=WIPxdb2P8sAC.
DW Trigg and A Gi Leach. Exponential smoothing with an adaptive response rate. Journal of the Operational Research Society, 18:53–59, 1967.
Charles C Holt. Forecasting seasonals and trends by exponentially weighted moving averages. International journal of forecasting, 20(1):5–10, 2004.
Peter R Winters. Forecasting sales by exponentially weighted moving averages. Management science, 6(3):324–342, 1960.
R. Hyndman, A.B. Koehler, J.K. Ord, and R.D. Snyder. Forecasting with Exponential Smoothing: The State Space Approach. Springer Series in Statistics. Springer Berlin Heidelberg, 2008. ISBN 9783540719182. URL: https://books.google.com.co/books?id=GSyzox8Lu9YC.
Herman Wold. A study in the analysis of stationary time series. PhD thesis, Almqvist & Wiksell, 1938.
George Udny Yule. On a method of investigating periodicities in disturbed series with special reference to wolfer’s sunspot numbers. Statistical Papers of George Udny Yule, pages 389–420, 1971.
Søren Bisgaard and Murat Kulahci. Time series analysis and forecasting by example. John Wiley & Sons, 2011.
P.J. Brockwell and R.A. Davis. Time Series: Theory and Methods: Theory and Methods. Springer Series in Statistics. Springer New York, 1991. ISBN 9780387974293. URL: https://books.google.com.co/books?id=ZW_ThhYQiXIC.
Casimir Michael Stralkowski. Lower order autoregressive-moving average stochastic models and their use for the characterization of abrasive cutting tools. PhD thesis, University of Wisconsin, 1968.
George Udny Yule. Vii. on a method of investigating periodicities disturbed series, with special reference to wolfer's sunspot numbers. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 226(636-646):267–298, 1927.
Gilbert Thomas Walker. On periodicity in series of related terms. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 131(818):518–532, 1931.
Maurice H Quenouille. Approximate tests of correlation in time-series 3. In Mathematical Proceedings of the Cambridge Philosophical Society, volume 45, 483–484. Cambridge University Press, 1949.
GM Jenkins and others. Tests of hypotheses in the linear autoregressive model. 1956.
HE Daniels. The approximate distribution of serial correlation coefficients. Biometrika, 43(1/2):169–185, 1956.
George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. Time series analysis: forecasting and control. John Wiley & Sons, 2015.
Wei William and S Wei. Time series analysis: univariate and multivariate methods. USA, Pearson Addison Wesley, Segunda edicion. Cap, 10:212–235, 2006.
George C Tiao and George EP Box. Modeling multiple time series with applications. journal of the American Statistical Association, 76(376):802–816, 1981.
Ruey S Tsay and George C Tiao. Consistent estimates of autoregressive parameters and extended sample autocorrelation function for stationary and nonstationary arma models. Journal of the American Statistical Association, 79(385):84–96, 1984.
Johannes Ledolter and Bovas Abraham. Some comments on the initialization of exponential smoothing. Journal of Forecasting, 3(1):79–84, 1984.
S. Theodoridis. Machine Learning: A Bayesian and Optimization Perspective. Elsevier Science, 2020. ISBN 9780128188040. URL: https://books.google.com.co/books?id=l-nEDwAAQBAJ.
J. Brownlee and Machine Learning Mastery. Deep Learning with Python: Develop Deep Learning Models on Theano and TensorFlow Using Keras. Machine Learning Mastery, 2017. URL: https://books.google.com.co/books?id=eJw2nQAACAAJ.
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning representations by back-propagating errors. nature, 323(6088):533–536, 1986.
Arthur E Bryson Jr, Walter F Denham, and Stewart E Dreyfus. Optimal programming problems with inequality constraints. AIAA journal, 1(11):2544–2550, 1963.
Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026, 2013.
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing, 6645–6649. Ieee, 2013.
Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber. Lstm: a search space odyssey. IEEE transactions on neural networks and learning systems, 28(10):2222–2232, 2016.
Rafal Jozefowicz, Wojciech Zaremba, and Ilya Sutskever. An empirical exploration of recurrent network architectures. In International conference on machine learning, 2342–2350. PMLR, 2015.
Ilya Sutskever, James Martens, and Geoffrey E Hinton. Generating text with recurrent neural networks. In Proceedings of the 28th international conference on machine learning (ICML-11), 1017–1024. 2011.
Shujie Liu, Nan Yang, Mu Li, and Ming Zhou. A recursive recurrent neural network for statistical machine translation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1491–1500. 2014.
Alex Graves and Navdeep Jaitly. Towards end-to-end speech recognition with recurrent neural networks. In International conference on machine learning, 1764–1772. PMLR, 2014.
Andrej Karpathy and Li Fei-Fei. Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE conference on computer vision and pattern recognition, 3128–3137. 2015.
Youngjoo Seo, Manuel Morante, Yannis Kopsinis, and Sergios Theodoridis. Unsupervised pre-training of the brain connectivity dynamic using residual d-net. In Neural Information Processing: 26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12–15, 2019, Proceedings, Part III 26, 608–620. Springer, 2019.
Tiago E Pratas, Filipe R Ramos, and Lihki Rubio. Forecasting bitcoin volatility: exploring the potential of deep learning. Eurasian Economic Review, pages 1–21, 2023.