Automation, Control and Intelligent Systems
Volume 1, Issue 4, August 2013, Pages: 90-98
Received: Jul. 16, 2013;
Published: Aug. 10, 2013
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Sonja Pravilović, Montenegro Business School, "Mediterranean" University, Podgorica, Montenegro
Annalisa Appice, Dipartimento di Informatica, Università degli Studi di Bari Aldo Moro, Bari, Italy
Automatic forecasts of univariate time series are largely demanded in business and science. In this paper, we investigate the forecasting task for geo-referenced time series. We take into account the temporal and spatial dimension of time series to get accurate forecasting of future data. We describe two algorithms for forecasting which ARIMA models. The first is designed for seasonal data and based on the decomposition of the time series in seasons (temporal lags). The ARIMA model is jointly optimized on the temporal lags. The second is designed for geo-referenced data and based on the evaluation of a time series in a neighborhood (spatial lags). The ARIMA model is jointly optimized on the spatial lags. Experiments with several time series data investigate the effectiveness of these temporal- and spatial- aware ARIMA models with respect to traditional one.
The Intelligent Forecasting Model of Time Series, Automation, Control and Intelligent Systems.
Vol. 1, No. 4,
2013, pp. 90-98.
J.S. Armstrong, "Combining forecasts: the end of the beginning or the beginning of the end?", Int. Journal Forecast. 5, 1989, pp.585–588,
G.E.P. Box, G.M. Jenkins, Time series analysis: Forecasting and control. Holden Day, San Francisco, 1970.
P.J. Brockwell, R.A. Davis, Time Series: Theory and Methods, 2nd ed. Springer, 2009.
D.W. Bunn, "Combining forecasts", European Journal of the Operational Reseach. 33, 1988, pp. 223-229.
N.A. Gershenfeld, A.S. Weigand, "The Future of Time Series", Learning and Understanding. Time Series Prediction. Forecasting the Future and Understanding the Past. In: Eds. A.S.Wigand and Gersehenfeld, N.A. SFl Studies in the Sciences Complexity, vol. IV, Addison Wesley, 1993, pp. 1-70.
S.L. Ho, "The use of ARIMA models for reliability forecasting and analyses", Computers and industrial engineering. 35 (1-2), 1998. pp. 213-216.
R.J. Hyndman, Data from the M-Competitions. R package version 1.11, http://CRAN.R-project.org/package= forecasting , 2008.
R.J. Hyndman, M. Akram, B.C., Archibald, "The Admissible Parameter Space for Exponential Smoothing Models", Annals of the Institute of Statistical Mathematics, 60 (2), 2008, pp. 407-426.
R.J. Hyndman, Y. Khandakar, "Automatic time series forecasting", The forecast package for R. Journal of Statistical Software, 26(3), 2008.
R.J. Hyndman, "Data Sets from\Forecasting: Methods and Applications By Makridakis", Wheelwright & Hyndman 1998, R package version 1.11.http://CRAN.R-project.org/ package=forecasting, 2008.
R.J. Hyndman, "Forecasting Functions for Time Series", R package version 1.11, http://CRAN.R-project.org/package =forecasting , 2008.
P. Newbold, "ARIMA model building and the time-series analysis approach to forecasting", Journal Fore cast. 2, 1983, pp. 23–35.
J.W. Taylor, "Short-term electricity demand forecasting using double seasonal exponential smoothing", Journal of the Operational Research Society. 54, 2003, pp. 799-805.
A. Timmermann, Chapter 4: forecast combinations. Handbook. Econ. Forecast. 1, 2006.
G.U. Yule, "On the method of investigating periodicities in disturbed series, with special reference to Wolfer's sunspot numbers", Philos. Trans. Roy. Soc. London Ser. A 226, 1927, pp. 267–298.
O. Ohashi, L. Torgo, Wind speed forecasting using spatio-temporal indicators. In L. D. Raedt, C. Bessiere, D. Dubois, P. Doherty, P. Frasconi, F. Heintz, P. J. F. Lucas, editors, 20th European Conference on Artiﬁcial Intelligence. Including Prestigious Applications of Artiﬁcial Intelligence (PAIS-2012), SystemDemonstrations Track, volume 242 of Frontiers in Artiﬁcial Intelligence and Applications, pp. 975–980. IOS Press, 2012.
W. Tobler., A computer movie simulating urban growth in the Detroit region". Economic Geography, 46(2), 2012, pp. 234-240.
M. F. Goodchild, Spatial autocorrelation. Norwich, England: GeoBooks. 1986.
Y. C Lee, L. Tong, Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming. Knowledge- Based Systems, (24), 2011, pp. 66–72.
P.A.P. Moran, "Notes on Continuous Stochastic Phenomena," Biometrika, 37, 1950, pp. 17–23.
C.D. Keeling, T. P. Whorf, "Scripps Institution of Oceanography (SIO)", University of California, La Jolla, California USA 92093-0220.