Abstract
The aim of this paper is to present a new way of multidimensional data visualization for explorative forecast built for real meteorological data coming from the Institute of Meteorology and Water Management (IMGW) in Katowice, Poland. In the earlier works two first authors of the paper proposed a method that aggregates huge amount of data based on fuzzy numbers. Explorative forecast uses similarity of data describing situations in the past to those in the future. 2D and 3D visualizations of multidimensional data can be used to carry out its analysis to find hidden information that is not visible in the raw data e.g. intervals of fuzziness, fitting real number to a fuzzy number.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Armstrong, J.S.: Principles of Forecasting, pp. 1–12. Kluwer Academic Publishers, Norwell (2002) ISBN 0-306-47630-4
Beyer, K.S., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is ”Nearest Neighbour” meaningful? In: Proc. of the 7th Int. Conf. on Database Theory, pp. 217–235 (1999)
Bloch, I., Maitre, H.: Fuzzy Mathematical Morphologies: a Comparative Study. Pattern Recognition 28(9), 1341–1387 (1995)
Domańska, D., Wojtylak, M.: Application of Fuzzy Time Series Models for Forecasting Pollution Concentrations. Expert Systems with Applications 39(9), 7673–7679 (2012)
Domańska, D., Wojtylak, M.: Change a Sequence into a Fuzzy Number. In: Cao, L., Zhong, J., Feng, Y. (eds.) ADMA 2010, Part II. LNCS, vol. 6441, pp. 55–62. Springer, Heidelberg (2010)
Domańska, D., Wojtylak, M.: Fuzzy Weather Forecast in Forecasting Pollution Concentrations. In: Proc. of Chaotic Modeling and Simulation International Conference, CD Version (2010)
Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis. Prentice-Hall, Englewood Cliffs (1992)
Koronacki, J., Ćwik, J.: Statistical Learning Systems. Ed. 2, Exit, Warszawa (2008) (in Polish)
Ou, G., Murphey, Y.L.: Multi-class Pattern Classification using Neural Networks. Pattern Recognition 40(1), 4–18 (2007)
Papageorgiou, E.I.: A New Methodology for Decisions in Medical Informatics using Fuzzy Cognitive Maps Based on Fuzzy Rule-extraction Techniques. Applied Soft Computing 11(1), 500–513 (2011)
Sammon, J.W.: A Nonlinear Mapping for Data Structure Analysis. IEEE Transactions on Computers 18(5), 401–409 (1969)
Sanyal, J., Dyer, S.Z., Mercer, J., Amburn, A., Moorhead, P., Noodles, R.J.: Noodles: A Tool for Visualization of Numerical Weather Model Ensemble Uncertainty. IEEE Transactions on Visualization and Computer Graphics 16(6), 1421–1430 (2010)
Shepard, R.N., Carroll, J.D.: Parametric Representation of Nonlinear Data Structures. In: Krishnaiah, P.R. (ed.) Proceedings of the International Symposium on Multivariate Analysis, pp. 561–592. Academic, New York (1965)
Wu, S., Chow, T.W.S.: PRSOM: a New Visualization Method by Hybridizing Multidimensional Scaling and Self-organizing Map. IEEE Transactions on Neural Networks 16(6), 1362–1380 (2005)
Zadeh, L.: Fuzzy Sets. Information and Control 8(3), 338–353 (1965)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Domańska, D., Wojtylak, M., Kotarski, W. (2012). Visualization of Multidimensional Data in Explorative Forecast. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2012. Lecture Notes in Computer Science, vol 7594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-33564-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33563-1
Online ISBN: 978-3-642-33564-8
eBook Packages: Computer ScienceComputer Science (R0)