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has been cited by the following article:

• TITLE: Forecasting Outlier Occurrence in Stock Market Time Series Based on Wavelet Transform and Adaptive ELM Algorithm

  AUTHORS: Nargess Hosseinioun

  KEYWORDS: Component, Extreme Learning Machine, Outliers Forecasting, Wavelet Transform

  JOURNAL NAME: Journal of Mathematical Finance, Vol.6 No.1, February 26, 2016

  ABSTRACT: In financial field, outliers represent volatility of stock market, which plays an important role in management, portfolio selection and derivative pricing. Therefore, forecasting outliers of stock market is of the great importance in theory and application. In this paper, the problem of predicting outliers based on adaptive ensemble models of Extreme Learning Machines (ELMs) is considered. We found out that the proposed model is applicable for outlier forecasting and outperforms the methods based on autoregression (AR) and extreme learning machine (ELM) models.