An Empirical Mode Decomposition Based Method to Synthesize Ensemble Multidimensional Gaussian Neuro-Fuzzy Models in Financial Forecasting
| Autor: | Nataliia Vlasenko, Alexander Vlasenko, Dmytro Peleshko, Olena Vynokurova |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Communications in Computer and Information Science ISBN: 9783030616557 DSMP |
| DOI: | 10.1007/978-3-030-61656-4_9 |
| Popis: | Time series arise in different fields of the economy and forecasting of them is important part of decision making. However, their intrinsic complexity and nonlinear behavior makes prediction in that field a challenging task. Hybrid artificial intelligence models are among the most powerful tools in handling such complex dynamics. This paper introduces a novel model which uses the empirical mode decomposition as a denoising and decomposition framework for the ensemble multidimensional Gaussian based neuro-fuzzy model in order to achieve better accuracy. The computational experimental results show clear advantages of the proposed approach - better prediction accuracy, faster error decay with preserving of the generalization abilities and reasonable computational overhead. |
| Databáze: | OpenAIRE |
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