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Soft Computing
Erschienen in: Soft Computing 7/2017

21.09.2015 | Methodologies and Application

Predicting agent-based financial time series model on lattice fractal with random Legendre neural network

verfasst von: Anqi Pei, Jun Wang, Wen Fang

Erschienen in: Soft Computing | Ausgabe 7/2017

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Abstract

An agent-based financial price model is developed by percolation system on the Sierpinski carpet lattice, in an attempt to reproduce and investigate fluctuation behaviors of price changes in the financial market. The percolation theory is usually used to describe the behaviors of connected clusters in a random graph, and the Sierpinski carpet lattice is an infinitely ramified fractal. We forecast and investigate the stock prices of the financial model by an improved Legendre neural network–Legendre neural network with random time strength function (LeNNRT). To test the LeNNRT and study the fluctuation behaviors of the stock prices on different time lag, the k-day moving average of Shanghai Composite Index and the simulated price series of the proposed model are predicted by the LeNNRT model. We exhibit the predictive results and compare the forecasting accuracies with different values of k for both the real data and the simulated data.
Vorheriger Artikel Ecosystem particle swarm optimization
Nächster Artikel An improved problem aware local search algorithm for the DNA fragment assembly problem

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Metadaten
Titel
Predicting agent-based financial time series model on lattice fractal with random Legendre neural network
verfasst von
Anqi Pei
Jun Wang
Wen Fang
Publikationsdatum
21.09.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 7/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-015-1874-3

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