Intuitionistic Fuzzy Neural Network for Time Series Forecasting - The Case of Metal Prices - Inria - Institut national de recherche en sciences et technologies du numérique
Conference Papers Year : 2021

Intuitionistic Fuzzy Neural Network for Time Series Forecasting - The Case of Metal Prices

Petr Hajek
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  • PersonId : 992409
Vladimir Olej
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  • PersonId : 992410
Wojciech Froelich
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  • PersonId : 1105466
Josef Novotny
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  • PersonId : 1105467

Abstract

Forecasting time series is an important problem addressed for years. Despite that, it still raises an active interest of researchers. The main issue related to that problem is the inherent uncertainty in data which is hard to be represented in the form of a forecasting model. To solve that issue, a fuzzy model of time series was proposed. Recent developments of that model extend the level of uncertainty involved in data using intuitionistic fuzzy sets. It is, however, worth noting that additional fuzziness exhibits nonlinear behavior. To cope with that issue, we propose a time series model that represents both high uncertainty and non-linearity involved in the data. Specifically, we propose a forecasting model integrating intuitionistic fuzzy sets with neural networks for predicting metal prices. We validate our approach using five financial multivariate time series. The results are compared with those produced by state-of-the-art fuzzy time series models. Thus, we provide solid evidence of high effectiveness of our approach for both one- and five-day-ahead forecasting horizons.
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hal-03287696 , version 1 (15-07-2021)

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Petr Hajek, Vladimir Olej, Wojciech Froelich, Josef Novotny. Intuitionistic Fuzzy Neural Network for Time Series Forecasting - The Case of Metal Prices. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.411-422, ⟨10.1007/978-3-030-79150-6_33⟩. ⟨hal-03287696⟩
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