Abstract
The task being addressed in this paper consists of trying to forecast the future value of a time series variable on a certain geographical location, based on historical data of this variable collected on both this and other locations. In general, this time series forecasting task can be performed by using machine learning models, which transform the original problem into a regression task. The target variable is the future value of the series, while the predictors are previous past values of the series up to a certain p-length time window. In this paper, we convey information on both the spatial and temporal historical data to the predictive models, with the goal of improving their forecasting ability. We build technical indicators, which are summaries of certain properties of the spatio-temporal data, grouped in the spatio-temporal clusters and use them to enhance the forecasting ability of regression models. A case study with air temperature data is presented.
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References
Dubin, R.A.: Spatial autocorrelation: A primer. Journal of Housing Economics 7, 304–327 (1998)
Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining data streams: A review. ACM SIGMOD Record 34(2), 18–26 (2005)
Getis, A.: A history of the concept of spatial autocorrelation: A geographer’s perspective. Geographical Analysis 40(3), 297–309 (2008)
Goodchild, M.: Spatial autocorrelation. Geo Books (1986)
Holden, Z.A., Evans, J.S.: Using fuzzy c-means and local autocorrelation to cluster satellite-inferred burn severity classes. International Journal of Wildland Fire 19(7), 853–860 (2010)
LeSage, J., Pace, K.: Spatial dependence in data mining. In: Data Mining for Scientific and Engineering Applications, pp. 439–460. Kluwer Academic Publishing (2001)
Ohashi, O., Torgo, L.: Wind speed forecasting using spatio-temporal indicators. In: Raedt, L.D., Bessière, C., Dubois, D., Doherty, P., Frasconi, P., Heintz, F., Lucas, P.J.F. (eds.) 20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS 2012), System Demonstrations Track, vol. 242, pp. 975–980. IOS Press (2012)
Rousseeuw, P.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20(1), 53–65 (1987)
Scrucca, L.: Clustering multivariate spatial data based on local measures of spatial autocorrelation. Technical Report 20, Quaderni del Dipartimento di Economia, Finanza e Statistica, Università di Perugia (2005)
Takens, F.: Detecting strange attractors in turbulence. Dynamical Systems and Turbulence Warwick 898(1), 366–381 (1981)
Wang, Y., Witten, I.: Induction of model trees for predicting continuous classes. In: Proc. Poster Papers of the European Conference on Machine Learning, pp. 128–137. Faculty of Informatics and Statistics, Prague (1997)
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Appice, A., Pravilovic, S., Malerba, D., Lanza, A. (2013). Enhancing Regression Models with Spatio-temporal Indicator Additions. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds) AI*IA 2013: Advances in Artificial Intelligence. AI*IA 2013. Lecture Notes in Computer Science(), vol 8249. Springer, Cham. https://doi.org/10.1007/978-3-319-03524-6_37
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DOI: https://doi.org/10.1007/978-3-319-03524-6_37
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03523-9
Online ISBN: 978-3-319-03524-6
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