Abstract
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.
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References
Abarbanel, H.: Analysis of Observed Chaotic Data. Springer, New York (1996)
Bishop, C.: Neural Networks for Pattern Recognition. Oxford Univ. Press Inc., N.Y (1995)
Chen, C.H., Hong, T.P., Tseng, V.S.: Fuzzy data mining for time-series data. Applied Soft Computing 12(1), 536–542 (2012)
Ghahramani, Z.: Solving inverse problems using an em approach to density estimation. In: Proceedings of the 1993 Connectionist Models Summer School. Erlbaum Ass., Hillsdale (1994)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)
Haykin, S., Principe, J.: Making sense of a complex world. IEEE Signal Processing Magazine, 66–81 (1998)
Huang, C.F.: A hybrid stock selection model using genetic algorithms and support vector regression. Applied Soft Computing 12(2), 807–818 (2012)
Khashei, M., Bijari, M.: A new class of hybrid models for time series forecasting. Expert Systems with Applications 39(4), 4344–4357 (2012)
Masulli, F., Studer, L.: Time series forecasting and neural networks. In: Invited tutorial in Proc. of IJCNN 1999, Washington D.C., U.S.A. (1999)
Panella, M.: Advances in biological time series prediction by neural networks. Biomedical Signal Processing and Control 6(2), 112–120 (2011)
Panella, M., Barcellona, F., D’Ecclesia, R.: Forecasting energy commodity prices using neural networks. Advances in Decision Sciences 2012, 1–26 (2012)
Panella, M., Liparulo, L., Barcellona, F., D’Ecclesia, R.: A study on crude oil prices modeled by neurofuzzy networks. In: Proceedings of FUZZ-IEEE 2013, Hyderabad, India (2013)
Panella, M., Rizzi, A., Martinelli, G.: Refining accuracy of environmental data prediction by MoG neural networks. Neurocomputing 55(3-4), 521–549 (2003)
Rhodes, C., Morari, M.: The false nearest neighbors algorithm: An overview. Computers & Chemical Engineering 21(suppl.), S1149 – S1154 (1997), http://www.sciencedirect.com/science/article/pii/S0098135497876570 , supplement to Computers and Chemical Engineering 6th International Symposium on Process Systems Engineering and 30th European Symposium on Computer Aided Process Engineering
Tikhonov, A., Arsenin, V.: Solutions of Ill-posed Problems. W.H. Winston Ed. (1977)
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Panella, M., Liparulo, L., Proietti, A. (2015). Time Series Analysis by Genetic Embedding and Neural Network Regression. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_3
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DOI: https://doi.org/10.1007/978-3-319-18164-6_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18163-9
Online ISBN: 978-3-319-18164-6
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