Designing ANN forecasting architectures from data conflict plots

One of the main issues in the analysis of a time series is its forecasting. Many questions arise in the design of a neural network that aims to capture the dynamics of a temporal sequence in order to predict it. In a reproducible way we want to find decision strategies for the preprocessing and the architecture of the network. In this paper we introduce a novel technique to extract important data features, called the data conflict plot. The conflict plot is used to design a modified architecture for the prediction of signals with distinct periodic components. Instead of a single delay line, this architecture is preceded by several incompletely connected delay lines