Abstract
One of the main problems that arises when dealing with time series is the existence of missing values which have to be completed previously to every statistical treatment. Here we present several models based on neural networks (NNs) to fill the missing periods of data within a total ozone (TO) time series. These non linear models have been compared with linear techniques and better results are obtained by using the non linear ones. A neural network scheme suitable for TO monthly values prediction is also presented.
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© 2003 Springer-Verlag Berlin Heidelberg
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Monge-Sanz, B., Medrano-Marqués, N. (2003). Artificial Neural Networks Applications for Total Ozone Time Series. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_102
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DOI: https://doi.org/10.1007/3-540-44869-1_102
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