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Research on time series forecasting has been an area of considerable interest in recent decades. Several techniques have been researched for time series forecasting. There is a fundamental task in any area of knowledge of time series: use past values to predict future values from the available historical series. Thus, a very important step is to define which of these past values will be considered in the prediction process. In this paper it is proposed two hybrid systems to select variables: Harmony Search and Neural Networks (HS + MLP) and Temporal Memory Search and Neural Networks (TMS + MLP). The variables selections improves the performance of learning models by eliminating redundant or irrelevant attributes. To perform a comparative study between the techniques, ten real-world time series were used.