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A powerful tool for fitting and forecasting deterministic and stochastic processes: The Kohonen classification

  • Part VII: Prediction, Forecasting and Monitoring
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

In this paper, we propose a general approach for fitting and forecasting the behavior of time-dependent processes. The only hypothesis on which it is based is the stationarity of the process dynamics. The approach is clearly non-parametric, uses no kind of a priori hypothesis on the form of the process and reveals itself powerful on either deterministic processes (such linear, logarithmic or sinusoidal ones) or stochastic ones (being able to reproduce even a white noise). The fields of applications of the proposed methods are time-series prevision but also risk analysis, allowing to determine the limits between which a stochastic process will behave on a specific time-horizon.

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Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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de Bodt, E., Grégoire, P., Cottrell, M. (1997). A powerful tool for fitting and forecasting deterministic and stochastic processes: The Kohonen classification. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020280

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  • DOI: https://doi.org/10.1007/BFb0020280

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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