A comparison between SARIMA models and Feed Forward Neural Network Ensemble Models for Time Series Data

In this paper, we investigate the robustness of Feed Forward Neural Network (FFNN) ensemble models applied to quarterly time series forecasting tasks, by comparing their prediction ability with that of Seasonal Auto-regressive Integrated Moving Average (SARIMA) models. We obtained adequate SARIMA models which required statistical knowledge and considerable effort. On the other hand, FFNN ensemble models were readily constructed from a single FFNN template, and they produced competitive forecasts, at the level of well-constructed SARIMA models. The single template approach for adapting FFNN ensembles to multiple time series datasets can be an economic and sensible alternative if fitting individual models for each time series turns out to be very time consuming. Additionally, FFNN ensembles were able to produce accurate interval estimations, in addition to good point forecasts.