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Fractional-Order Memristive Predictor: Arbitrary-Order String Scaling Fracmemristor Based Prediction Model of Trading Price of Future
IEEE Intelligent Systems ( IF 5.6 ) Pub Date : 2020-03-01 , DOI: 10.1109/mis.2020.2974201
Yi-Fei Pu , Ni Zhang , Huai Wang

In this article, inspired by the state-of-the-art research progress of the fractional-order memristor, a fractional-order memristive prediction model of the trading price of future is attempted to be proposed, which can feasibly predict the variation trend of the following unknown trading price data only depending on a small sampling of the given ones in a previous short time. At first, the analogy analysis of the relationship between an actual system of future trading and a physical memristive system of charge transfer is achieved. Second, the achievement of a corresponding capacitive string scaling fracmemristor (LCSF) is mathematically derived and analyzed in detail. Third, a 5-years data from 2015 to 2019 of the 300 exchange traded fund open-end index securities investment fund of Shanghai Stock Exchange is selected to verify the multiscale prediction ability of trading price of future of the LCSF. The theoretical contribution of this article is the first application of the fractional-order memristive electronic system to feasibly achieve an intelligent prediction model of the financial technology.

中文翻译:

分数阶忆阻预测器:基于任意阶串标度分数阶忆阻器的期货交易价格预测模型

在本文中,受分数阶忆阻器最新研究进展的启发,尝试提出一种分数阶忆阻器期货交易价格预测模型,该模型可以切实预测期货交易价格的变化趋势。以下未知的交易价格数据仅取决于之前短时间内给定的小样本。首先,实现了期货交易实际系统与电荷转移物理忆阻系统之间关系的类比分析。其次,从数学上推导出并详细分析了相应的电容串标度裂变电阻器 (LCSF) 的实现。第三,选取上交所300只交易所交易基金开放式指数证券投资基金2015-2019年5年数据,验证LCSF期货交易价格的多尺度预测能力。本文的理论贡献是首次应用分数阶忆阻电子系统,切实实现金融科技的智能预测模型。
更新日期:2020-03-01
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