Moments of Predictive Deviations for Ensemble Diversity Measures to Estimate the Performance of Time Series Prediction
This paper presents an analysis of moments of predictive deviations as measures of ensemble diversity to estimate the performance of time series prediction. As an extension of the ambiguity decomposition of bagging ensemble, we decompose the fourth power of ensemble prediction error and clarify the effect of the moments of predictive deviations of ensemble members to the ensemble prediction error. We utilize this analysis for estimating the performance of time sires prediction, and show the effectiveness by means of numerical experiments.
KeywordsMoments of predictive deviations Ensemble diversity measures Performance estimation Time series prediction
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- 2.Chen, H.: Diversity and Regularization in Neural Network Ensembles. PHD thesis, University of Birmingham (2008)Google Scholar
- 4.Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proceedings of the Fourteenth International Conference 18 on Artificial Intelligence (IJCAI), pp. 1137–1143 (1995)Google Scholar
- 6.Breiman, L.: Bagging Predictors. Mach. Learn. 26, 123–140 (1996)Google Scholar
- 7.Aihara, K.: Theories and Applications of Chaotic Time Series Analysis, Sangyo Tosho, Tokyo (2000)Google Scholar