Ensembles of neural networks for time series with application to climate change prediction

Abstract

Ensembles of artificial neural networks combining the outputs of individual time series models may have the potential to improve overall predictive performance. Deep and modular artificial neural networks are among recently developed machine learning techniques that have been successfully applied across various domains ranging from speech recognition to image classification. Climate change prediction information is important for planning and managing the impact of global change. However, the generation of climate change predictions from physical or numerical models is computationally very intensive, often requiring supercomputing processing capabilities and producing very large volumes of data. This research focuses on the application of various ensembles of architectures of artificial neural networks (ANNs) to time series. These ensembles are applied to the outputs of six different physical climate change prediction models. The output of these ensembles can be viewed as the consensual output of the individual artificial neural network prediction models. Six different climate change prediction models are considered for the area, Addis Ababa in Ethiopia. A single parameter, namely, the maximum predicted temperature (MaxTemp) aggregated over a quarterly period is studied. An artificial neural network is individually trained on the output of one of the six climate change prediction models. The predictive performance of different ensembles of these trained ANNs are compared to the actual averaged outputs of the climate change models. Results show that some ensembles have good predictive fidelity compared with the individual model outputs