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Prediction of stocks requires a lot of knowledge on market share values and trends. This knowledge can be obtained by experience in this particular field. For a normal human it requires a lot of time and energy to gain experience to predict trends in stock prices. With advancement in technology, machine learning algorithms keep the capability of predicting trends in stocks because of the huge computational capacity which is available nowadays. In this paper, hybrid ARIMA (Auto Regressive-Integrated-Moving Average)-GRU(GatedRecurrent-Unit) model has been proposed which learns continuously from the history of stocks invested, sold and brought by the clients. ARIMA-GRU model identifies patterns, analyzes which kind of data is suitable for prediction and then predicts suitable value. A detailed information about working of this model, parameters it considers and type of values it predicts have been discussed in this paper. Keywords— Autoregressive integrated moving average, gated recurrent unit, Stock price prediction, Hybrid models

Stock Prediction using Hybrid ARIMA and GRU Models