In 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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