论文| Yi-Fei PU, Ni ZHANG, and Huai WANG：Fractional-Order Memristive Predictor: Arbitrary-Order String Scaling Fracmemristor Based Prediction Model of Trading Price of Future

本文（Fractional-Order Memristive Predictor: Arbitrary-Order String Scaling Fracmemristor Based Prediction Model of Trading Price of Future）原载IEEE Intelligent Systems由四川大学计算机学院蒲亦非教授等科研人员创作，系四川大学“智慧法治”超前部署学科系列学术成果。后续会持续分享四川大学“智慧法治”超前部署学科系列学术成果，欢迎大家阅读。

Abstract: 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.

Keywords: Charge transfer, Memristors, Frequency modulation, Autoregressive processes, Predictive models, Filtering, Laplace equations

Yi-Fei PU, Ni ZHANG, and Huai WANG. “Fractional-Order Memristive Predictor: Arbitrary-Order String Scaling Fracmemristor Based Prediction Model of Trading Price of Future,” IEEE Intelligent Systems, vol. 35, no. 2, pp. 65-77, 2020.（论文下载）