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Stock Price Prediction using Rule Based Genetic Algorithm Approach
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Securities trade data is a high dimensional time course of action cash related data that positions exceptional computational challenges. Stock data is variable with respect to time, suspecting the future example of the expenses is a trying task. The segments that effect the consistency of stock data can't be judged as the same variables may affect the estimation of the stock always. We propose a data burrowing approach for the desire of the advancement of securities trade. It consolidates using the innate estimation for pre taking care of and a cross breed packing strategy of Hierarchical gathering and Fuzzy C-Means for clustering. The genetic figuring helps in dimensionality diminish and packing makes highlight vectors that help with estimate.
Keywords
Fuzzy, C-Means, Stock, Prediction, Genetic Algorithm.
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