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Stock Market Forecasting Using ANFIS with OWA Operator

Gurbinder Kaur, Joydip Dhar, Rangan K. Guha


Stock market prediction is very interesting and yet very difficult job. Many techniques have been proposed for the prediction of stock prices. Furthermore, some drawbacks are found in the existing methods Firstly, statistical models are single variable methods and to build them some assumptions are necessary. Secondly, higher dimensional data can not easily processed by existing forecasting models, because model will become more complex with the increase of data dimensions. So to overcome all these drawbacks a new algorithm is proposed in this article, which employs a minimal variability order weighted averaging (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 subtractive clustering is used to forecast Bombay Stock Exchange Index (BSE30). Further, the proposed model is compared with some existing models. Results have shown that proposed model gives better forecasting than existing models.


Neural Networks, Fuzzy Logic, Forecasting, Stock Market, Subtractive clustering,

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