Minimal variability OWA operator combining ANFIS and fuzzy c-means for forecasting BSE index

Gurbinder Kaur, Joydip Dhar and Rangan Kumar Guha

Mathematics and Computers in Simulation (MATCOM), 2016, vol. 122, issue C, 69-80

Abstract: Stock data sets usually consist of many varied components or multiple periods of stock prices, resulting in a tedious stock market prediction using such high dimensional data. To reduce data dimensions, it is crucial to fuse high dimensional data into a useful forecasting factor without losing information contained in the original variables. Decision makers may desire low variability associated with a chosen weighting vector, further complicating proper weight assignment for past stock prices. In this paper a new time series algorithm is proposed to overcome above mentioned shortcomings, which employs a minimal variation order weighted average (OWA) operator to aggregate values of high dimensional data into a single attribute. Based on the proposed model a hybrid network based fuzzy inference system combined with fuzzy c-means clustering is used to forecast Bombay Stock Exchange Index (BSE30).

Keywords: OWA; Forecasting; Fuzzy logic; Neural network (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:122:y:2016:i:c:p:69-80

DOI: 10.1016/j.matcom.2015.12.001

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