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

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

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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