Results of the time series prediction competition at the Santa Fe Institute

By Andreas S. Weigend, Neil A. Gershenfeld. 1993

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Devon K. Barrow
2. Sven F. Crone
3. P. W. Tse
4. J. Urgen Schmidhuber
5. Liuhe Li
6. R. Yam
7. P. Tu
8. David R Ha

Citations in the corpus

• Deep learning in neural networks: An overview (2014, background): “Notably, an NN with internal delay lines won the Santa Fe time-series competition on chaotic intensity pulsations of an NH3 laser (Wan, 1994; Weigend and Gershenfeld, 1993).”
• A comparison of AdaBoost algorithms for time series forecast combination (2016, methodology): “applied a modification of the original AdaBoost algorithm in forecasting the laser data from the Santa Fe time series competition (Weigend and Gershenfeld 1993).”
• Intelligent Predictive Decision Support System for Condition-Based Maintenance (2001, result): “Recent work on forecasting nonlinear, non-stationary and non-Gaussian-type time series also indicates that recurrent neural networks (RNNs) have a better forecasting performance than other methods [8, 40 ,41].”