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Vilela LFS, Leme RC, Pinheiro CAM, Carpinteiro OAS. Forecasting financial series using clustering methods and support vector regression. Artif Intell Rev 2018. [DOI: 10.1007/s10462-018-9663-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Patra A, Das S, Mishra SN, Senapati MR. An adaptive local linear optimized radial basis functional neural network model for financial time series prediction. Neural Comput Appl 2015. [DOI: 10.1007/s00521-015-2039-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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ULLOA GUSTAVO, ALLENDE-CID HÉCTOR, ALLENDE HÉCTOR. ROBUST SIEVE BOOTSTRAP PREDICTION INTERVALS FOR CONTAMINATED TIME SERIES. INT J PATTERN RECOGN 2014. [DOI: 10.1142/s021800141460012x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Time series prediction is of primary importance in a variety of applications from several science fields, like engineering, finance, earth sciences, etc. Time series prediction can be divided in to two main tasks, point and interval estimation. Estimating prediction intervals, is in some cases more important than point estimation mainly because it indicates the likely uncertainty in the prediction process. Recently, the sieve bootstrap method has been successfully used in prediction of nonlinear time series. In this work, we study the performance of the prediction intervals based on the sieve bootstrap technique, which does not require the distributional assumption of normality as most techniques that are found in the literature. The construction of prediction intervals in the presence of different types of outliers is not robust from a distributional point of view, leading to an undesirable increase in the length of the prediction intervals. In the analysis of time series, it is common to have irregular observations that have different types of outliers. For this reason, we propose the construction of prediction intervals for returns based on the winsorized residual and bootstrap techniques for time series prediction. We propose a novel, simple and distribution free resampling technique for developing robust prediction intervals for returns and volatilities for GARCH models. The proposed procedure is illustrated by an application to known synthetic and real-time series.
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Affiliation(s)
- GUSTAVO ULLOA
- Departamento de Informática, Universidad Técnica Federico Santa María, Avenida España 1680, Casilla 110-V, Valparaíso, Chile
| | - HÉCTOR ALLENDE-CID
- Departamento de Informática, Universidad Técnica Federico Santa María, Avenida España 1680, Casilla 110-V, Valparaíso, Chile
| | - HÉCTOR ALLENDE
- Departamento de Informática, Universidad Técnica Federico Santa María, Avenida España 1680, Casilla 110-V, Valparaíso, Chile
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