Affiliations: [a] Informatics Center, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, 50740-560, Recife-PE, Brazil | [b] Department of Systems and Computer, University of Pernambuco, Benfica, 455, Madalena, Recife-PE, Brazil | Federal University of Pernambuco, Recife, Brazil
Correspondence:
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Corresponding author: Ivna Valença, Informatics Center, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, 50740-560, Recife-PE, Brazil. Tel.: +55 81 8804 3472; E-mail: [email protected]
Abstract: A time series is a sequence of observations of a random variable. Hence, it is a stochastic process. Forecasting time series data is important component of operations research because these data often provide the foundation for decision models. This models are used to predict data points before they are measured based on known past events. Researches in this subject have been done in many areas like economy, energy production, ecology and others. To improve the process of time series forecasting it is important to identify which of past values will be considered to be used in the models by eliminating redundant or irrelevant attributes. Two hybrid systems Harmony Search with Neural Networks (HS) and Temporal Memory Search with Neural Networks (TMS) are improved and a new one is proposed: the Temporal Memory Search Limited with Neural Networks (TMSL). The performance of the techniques is investigated through an empirical evaluation on twenty real-world time series.
Keywords: Harmony search, neural networks, temporal memory search, time series forecasting, variable selection
