Nonlinear time series prediction based on a power-law noise model
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Details
| Original language | English |
|---|---|
| Pages (from-to) | 1839-1852 |
| Number of pages | 14 |
| Journal | International Journal of Modern Physics C |
| Volume | 18 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - Dec 2007 |
| Externally published | Yes |
| Publication type | A1 Journal article-refereed |
Abstract
In this paper we investigate the influence of a power-law noise model, also called noise, on the performance of a feed-forward neural network used to predict time series. We introduce an optimization procedure that optimizes the parameters the neural networks by maximizing the likelihood function based on the power-law model. We show that our optimization procedure minimizes the mean squared leading to an optimal prediction. Further, we present numerical results applying method to time series from the logistic map and the annual number of sunspots demonstrate that a power-law noise model gives better results than a Gaussian model.
ASJC Scopus subject areas
Keywords
- time series prediction, maximum likelihood, Monte Carlo method, feed-forward, neural network., SELF-ORGANIZED CRITICALITY, NEURAL-NETWORKS, OPTIMIZATION, EXPLANATION