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EnglishIn this paper, the time series forecasting problem is ap- proached by using a specic procedure to select the past samples of the sequence to be predicted, which will feed a suited function approx- imation model represented by a neural network. When the time series to be analysed is characterized by a chaotic behaviour, it is possible to demonstrate that such an approach can avoid an ill-posed data driven modelling problem. In fact, classical algorithms fail in the estimation of embedding parameters, especially when they are applied to real-world sequences. To this end we will adopt a genetic algorithm, by which each individual represents a possible embedding solution. We will show that the proposed technique is particularly suited when dealing with the pre- diction of environmental data sequences, which are often characterized by a chaotic behaviour.
In this paper, the time series forecasting problem is approached by using a specific procedure to select the past samples of the sequence to be predicted, which will feed a suited function approximation model represented by a neural network. When the time series to be analysed is characterized by a chaotic behaviour, it is possible to demonstrate that such an approach can avoid an ill-posed data driven modelling problem. In fact, classical algorithms fail in the estimation of embedding parameters, especially when they are applied to real-world sequences. To this end we will adopt a genetic algorithm, by which each individual represents a possible embedding solution. We will show that the proposed technique is particularly suited when dealing with the prediction of environmental data sequences, which are often characterized by a chaotic behaviour.
| Titolo: | Time series analysis by genetic embedding and neural network regression |
| Autori: | |
| Data di pubblicazione: | 2015 |
| Serie: | |
| Handle: | http://hdl.handle.net/11573/559207 |
| ISBN: | 978-3-319-18163-9 978-3-319-18164-6 |
| Appartiene alla tipologia: | 02a Capitolo o Articolo |
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| Dichiarazione_conformità 11-04-2015.pdf | dichiarazione | Altro materiale allegato | Tutti i diritti riservati (All rights reserved) | Administrator Richiedi una copia |
| Panella_Time_series_2016.pdf | Versione editoriale (versione pubblicata con il layout dell'editore) | Tutti i diritti riservati (All rights reserved) | Utenti riconosciuti Richiedi una copia |
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