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Evolving participatory learning fuzzy modeling for financial interval time series forecasting

机译:进化参与式学习模糊模型在金融区间时间序列预测中的应用

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Financial interval time series (ITS) describe the evolution of the maximum and minimum prices of an asset throughout time. These price ranges are related to the concept of volatility. Hence, their accurate forecasts play a key role in risk management, derivatives pricing and asset allocation, as well as supplements the information extracted by the time series of the closing price values. This paper addresses evolving fuzzy systems and financial ITS forecasting considering as the empirical application the main index of the Brazilian stock market, the IBOVESPA. An evolving participatory learning fuzzy model, named ePL-KRLS, is proposed. The model extends traditional ePL approach by considering Kernel functions to the identification of rule consequents parameters as well as a metaheuristic algorithm to automatically set model control parameters. One step ahead interval forecasts is compared against linear and nonlinear time series benchmark methods and with the state of the art evolving fuzzy models in terms of traditional accuracy metrics and quality measures designed for ITS. The results provide evidence for the predictability of of IBOVESPA ITS and significant forecast contribution of ePL-KRLS.
机译:财务间隔时间序列(ITS)描述了资产在整个时间内最高和最低价格的演变。这些价格范围与波动率的概念有关。因此,他们的准确预测在风险管理,衍生产品定价和资产分配中起着关键作用,并补充了收盘价值的时间序列提取的信息。考虑到作为经验应用的巴西股票市场的主要指标IBOVESPA,本文讨论了不断发展的模糊系统和金融ITS预测。提出了一种演化的参与式学习模糊模型,称为ePL-KRLS。该模型扩展了传统的ePL方法,将内核功能考虑到规则结果参数的识别以及用于自动设置模型控制参数的元启发式算法。在线性精度和非线性时间序列基准测试方法方面,将提前一个间隔的预测值与传统的针对ITS的准确性度量和质量度量的模糊模型进行了比较。结果为IBOVESPA ITS的可预测性和ePL-KRLS的重大预测贡献提供了证据。

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