Abstract
AbstractThe curse of dimensionality has long been a hurdle in the analysis of complex data in areas such as computational biology, ecology and econometrics. In this work, we present a forecasting algorithm that exploits the dimensionality of data in a nonparametric autoregressive framework. The main idea is that the dynamics of a chaotic dynamical system consisting of multiple time-series can be reconstructed using a combination of different variables. This nonlinear autoregressive algorithm uses multivariate attractors reconstructed as the inputs of a neural network to predict the future. We show that our approach, attractor ranked radial basis function network (AR-RBFN) provides a better forecast than that obtained using other model-free approaches as well as univariate and multivariate autoregressive models using radial basis function networks. We demonstrate this for simulated ecosystem models and a mesocosm experiment. By taking advantage of dimensionality, we show that AR-RBFN overcomes the shortcomings of noisy and short time-series data.
Funder
National Science Foundation
U.S. Department of Health & Human Services | National Institutes of Health
Publisher
Springer Science and Business Media LLC
Reference31 articles.
1. Reichman, O. J., Jones, M. B. & Schildhauer, M. P. Challenges and opportunities of open data in ecology. Science 331, 703–705 (2011).
2. Marx, V. Biology: The big challenges of big data. Nature 498, 255–260 (2013).
3. Boyd, D. & Crawford, K. Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society 15, 662–679 (2012).
4. Berndt, E. R., Hall, B. H., Hall, R. E. & Hausman, J. A. in Annals of Economic and Social Measurement, Volume 3, number 4 653–665 (NBER, 1974).
5. Ivancevic, V. G. & Ivancevic, T. T. Complex nonlinearity: chaos, phase transitions, topology change and path integrals. (Springer Science & Business Media, 2008).
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