Evolving sparsely connected neural networks for multi-step ahead forecasting

Abstract

Time Series Forecasting (TSF) is an important tool to sup- port decision making. Artificial Neural Networks (ANN) are innate candidates for TSF due to advantages such as nonlin- ear learning and noise tolerance. However, the search for the best ANN is a complex task that highly affects the forecast- ing performance. In this paper, we propose a novel Sparsely connected Evolutionary ANN (SEANN), which evolves more flexible ANN structures to perform multi-step ahead fore- casts. This approach is compared with a similar strategy but that only evolves fully connected ANNs (FEANN) and a conventional TSF method (i.e. ARIMA methodology). A set of six time series, from different real-world domains, was used in the comparison. Overall, the obtained results re- veal the proposed SEANN approach as the best forecasting method, optimizing more simpler structures and requiring less computational effort when compared with the fully con- nected evolutionary ANN strategy.