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.

Time series analysis by genetic embedding and neural network regression / Panella, Massimo; Liparulo, Luca; Proietti, Andrea. - STAMPA. - 37(2015), pp. 21-29. - SMART INNOVATION, SYSTEMS AND TECHNOLOGIES. [10.1007/978-3-319-18164-6_3].

Time series analysis by genetic embedding and neural network regression

PANELLA, Massimo;LIPARULO, LUCA;PROIETTI, ANDREA
2015

Abstract

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.
2015
Advances in Neural Networks: Computational and Theoretical Issues
978-3-319-18163-9
978-3-319-18164-6
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.
Time series prediction; embedding technique; genetic algorithm; environmental data
02 Pubblicazione su volume::02a Capitolo o Articolo
Time series analysis by genetic embedding and neural network regression / Panella, Massimo; Liparulo, Luca; Proietti, Andrea. - STAMPA. - 37(2015), pp. 21-29. - SMART INNOVATION, SYSTEMS AND TECHNOLOGIES. [10.1007/978-3-319-18164-6_3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/559207
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