Forecasting Univariate Time Series by Input Transformation and Selection of the Suitable Model

Several tasks in science, engineering, or financial are related with sequences of values throughout the time (time series). This paper is focused in univariate time series, so unknown future values are obtained from k previous (and known) values. To fit a model between independent variables (present and past values) and dependent variables (future values), Artificial Neural Networks, which are data driven, can get good results in its performance results. In this work, we present a method to find some alternatives to the ANN trained with the raw data. This method is based on transforming the original time series into the time series of differences between two consecutive values and the time series of increment (−1, 0, +1) between two consecutive values. The three ANN obtained can be applied in an individual way or combine to get a fourth alternative which result from the combination of the other. The method evaluates the performance of all alternatives and take the decision, on validation subset, which of the alternatives could improve the performance, on test subset of the ANN trained with raw data.