Ensemble encoding for time series forecasting with MLP networks

Neural networks represent a promising approach to time series forecasting; however, the problem of obtaining good network generalization continues to present a challenge. As a means of improving network generalization ability for time series forecasting applications, this paper investigates the utility of a biologically inspired scheme that employs receptive fields for encoding network inputs. Both single- and multi-step forecasting performance are studied in the context of the sunspot series. Additionally, a heuristic for selecting the placement and dilations of the receptive field functions is presented. The performance of multi-layered perceptron networks trained using the data arising from the encoding scheme is assessed. The heuristic for placing and dilating the receptive fields yielded networks that learn rapidly and have consistently good multi-step prediction capability as compared to other published results.