Article ID Journal Published Year Pages File Type
1139174 Mathematics and Computers in Simulation 2016 12 Pages PDF
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).

Related Topics
Physical Sciences and Engineering Engineering Control and Systems Engineering
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