@inproceedings{52377c540f894b3a9605e0edc1177b69,
title = "Local learning of tide level time series using a fuzzy approach",
abstract = "Forecasting the tide level in the Venezia lagoon is a very compelling task. In this work we propose a new approach to the learning of tide level time series based on the local learning procedure of Bottou and Vapnik, by considering the use of a fuzzy method for the selection of the closest patterns to the one to forecast. We made use also as learners of Support Vector Machines and of their ensembles based on Bagging and AdaBoost. The obtained forecasts of 500 randomly selected tide levels seem to be quite promising. Good performances are also noticed for forecasts of a set of 80 tide levels corresponding to exceptional periods with high tide and sea variabilities. The obtained forecasts of 80 selected tide levels compare very favorably with those of the baseline linear regressor model.",
author = "E. Canestrelli and P. Canestrelli and M. Corazza and M. Filippone and S. Giove and F. Masulli",
year = "2007",
doi = "10.1109/IJCNN.2007.4371233",
language = "English (US)",
isbn = "142441380X",
series = "IEEE International Conference on Neural Networks - Conference Proceedings",
pages = "1813--1818",
booktitle = "The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings",
note = "2007 International Joint Conference on Neural Networks, IJCNN 2007 ; Conference date: 12-08-2007 Through 17-08-2007",
}