Neural network ensemble operators for time series forecasting

Kourentzes, Nikos and Barrow, Devon and Crone, Sven (2014) Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41 (9). pp. 4235-4244. ISSN 0957-4174

[img]
Preview
PDF (Kourentzes et al. Neural Network Ensemble Operators for Time Series Forecasting)
Kourentzes_et_al._Neural_Network_Ensemble_Operators_for_Time_Series_Forecasting.pdf - Submitted Version

Download (274kB)

Abstract

The combination of forecasts resulting from an ensemble of neural networks has been shown to outperform the use of a single ``best'' network model. This is supported by an extensive body of literature, which shows that combining generally leads to improvements in forecasting accuracy and robustness, and that using the mean operator often outperforms more complex methods of combining forecasts. This paper proposes a mode ensemble operator based on kernel density estimation, which unlike the mean operator is insensitive to outliers and deviations from normality, and unlike the median operator does not require symmetric distributions. The three operators are compared empirically and the proposed mode ensemble operator is found to produce the most accurate forecasts, followed by the median, while the mean has relatively poor performance. The findings suggest that the mode operator should be considered as an alternative to the mean and median operators in forecasting applications. Experiments indicate that mode ensembles are useful in automating neural network models across a large number of time series, overcoming issues of uncertainty associated with data sampling, the stochasticity of neural network training and the distribution of the forecasts.

Item Type:
Journal Article
Journal or Publication Title:
Expert Systems with Applications
Additional Information:
The final, definitive version of this article has been published in the Journal, Expert Systems with Applications 41 (9), 2014, © ELSEVIER.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1706
Subjects:
ID Code:
67855
Deposited By:
Deposited On:
06 Dec 2013 11:03
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Oct 2020 02:20