Input variable selection for time series prediction with neural networks– an evaluation of visual, autocorrelation and spectral analysis for varying seasonality

The identification and selection of adequate input variables and lag structures without domain knowledge represents one the core challenges in modeling neural networks for time series prediction. Although a number of linear methods have been established in statistics and engineering, they provide limited insights for nonlinear patterns and time series without equidistant observations and shifting seasonal patterns of varying length, leading to model misspecification. This paper describes a heuristic process and stepwise refinement of competing approaches for model identification for multilayer perceptrons in predicting the ESTSP’07 forecasting competition time series.