Abstract
Meteorological forecasting is an important issue in research. Typically, the forecasting is performed at “global level,” by gathering data in a large geographical region and by studying their evolution, thus foreseeing the meteorological situation in a certain place. In this paper a “local level” approach, based on time series forecasting using Type-2 Fuzzy Systems, is proposed. In particular temperature forecasting is inspected. The Fuzzy System is trained by means of historical local time series. The algorithm uses a detrend procedure in order to extract the chaotic component to be predicted.
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Mencattini, A., Salmeri, M., Bertazzoni, S., Lojacono, R., Pasero, E., Moniaci, W.: Short term local meteorological forecasting using Type-2 fuzzy systems. In: Italian Workshop on Neural Network, Vietri sul Mare, Italy (2005)
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Mencattini, A., Salmeri, M., Bertazzoni, S., Lojacono, R., Pasero, E., Moniaci, W. (2006). Short Term Local Meteorological Forecasting Using Type-2 Fuzzy Systems. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_15
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DOI: https://doi.org/10.1007/11731177_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33183-4
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