Selection of representative feature training sets with self-organized maps for optimized time series modeling and prediction: application to forecasting daily drought conditions with ARIMA and neural network models

McCarthy, Elizabeth and Deo, Ravinesh C. and Li, Yan and Maraseni, Tek (2018) Selection of representative feature training sets with self-organized maps for optimized time series modeling and prediction: application to forecasting daily drought conditions with ARIMA and neural network models. In: Handbook of research on predictive modeling and optimization methods in science and engineering. Advances in Computational Intelligence and Robotics (ACIR) Book Series. IGI Publishing (IGI Global), Hershey, United States, pp. 446-464. ISBN 9781522547662

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Abstract

While the simulation of stochastic time series is challenging due to their inherently complex nature, this is compounded by the arbitrary and widely accepted feature data usage methods frequently applied during the model development phase. A pertinent context where these practices are reflected is in the forecasting of drought events. This chapter considers optimization of feature data usage by sampling daily data sets via self-organizing maps to select representative training and testing subsets and accordingly, improve the performance of effective drought index (EDI) prediction models. The effect would be observed through a comparison of artificial neural network (ANN) and an autoregressive integrated moving average (ARIMA) models incorporating the SOM approach through an inspection of commonly used performance indices for the city of Brisbane. This study shows that SOM-ANN ensemble models demonstrate competitive predictive performance for EDI values to those produced by ARIMA models.


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Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published chapter deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 07 Aug 2018 05:31
Last Modified: 02 Oct 2018 04:17
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.4018/978-1-5225-4766-2.ch020
URI: http://eprints.usq.edu.au/id/eprint/34657

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