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Soft Computing
Erschienen in: Soft Computing 10/2016

26.06.2015 | Methodologies and Application

An innovative recurrent error-based neuro-fuzzy system with momentum for stock price prediction

verfasst von: Mohammad Sultan Mahmud, Phayung Meesad

Erschienen in: Soft Computing | Ausgabe 10/2016

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Abstract

Neuro-fuzzy system is now one of the most widely used tools in the field of artificial intelligence systems. This study proposes a novel approach for time series stock market price prediction using a recurrent error-based neuro-fuzzy system with momentum (RENFSM). The basic idea of this approach is to use time series price momentum and time series prediction error adjusted to the well-known adaptive neuro-fuzzy inference system, ANFIS. Extended from ANFIS, the aim of this study is to propose a reliable prediction system with minimal error. Moreover, to evaluate the proposed model strength, four top-listed stocks from Dhaka stock exchange were applied. In the experiments, several choices of momentum from 3 to 20 days are selected for data preprocessing. It was found that the proposed RENFSM performed superiorly and was more reliable compared to the existing methods such as ANFIS and neural networks.
Vorheriger Artikel Relaxed Dijkstra and A* with linear complexity for robot path planning problems in large-scale grid environments
Nächster Artikel Heuristic approach for computer-aided lesion detection in mammograms

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Metadaten
Titel
An innovative recurrent error-based neuro-fuzzy system with momentum for stock price prediction
verfasst von
Mohammad Sultan Mahmud
Phayung Meesad
Publikationsdatum
26.06.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 10/2016
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-015-1752-z

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