Título: Swarm-Based Hybrid Intelligent Forecasting Method For Financial Time Series Prediction
Autores: Araújo, Ricardo de A.
Resumo: This paper presents a new approach, referred to as Swarm-based Hybrid Intelligent Forecasting (SHIF) method, for financial time series forecasting. It consists of a hybrid intelligent model composed of an Artificial Neural Network (ANN) and a Particle Swarm Optimizer (PSO), which determines the relevant time lags to represent a complex time series, as well as to evolve the structure and parameters of an ANN (pruning process) and its training algorithm (used to optimize the ANN weights supplied by the PSO). Initially, the proposed method chooses the best prediction model and posteriorly it uses a statistical behavioral test with a phase fix procedure to adjust time phase distortions (characterized by a one step delay of generated predictions regarding real time series values), where such distortions are commonly found in financial time series like. Furthermore, an experimental analysis is conducted with the proposed method using four real world financial time series, and the achieved results are discussed and compared, according to a group of five relevant performance metrics, to results found with previously models proposed in literature.
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Páginas: 18
Código DOI: 10.21528/lmln-vol5-no2-art5
Artigo em PDF: vol5-no2-art5.pdf
Arquivo BibTex: vol5-no2-art5.bib