RNNLBL : A Recurrent Neural Network and Log Bilinear based Efficient Stock Forecasting Model
Uma Prashant Gurav1, S. Kotrappa2

1Mrs. Uma Gurav*, Assistant Professor, Department of Computer Science and Engineering, V.T.U , Belgaum, India.
2Dr. Kotrappa S., Professor, Department of Computer Science and Engineering, K L E’s Dr MSS College of Engineering & Technology, Belgaum.
Manuscript received on January 22, 2020. | Revised Manuscript received on January 30, 2020. | Manuscript published on February 10, 2020. | PP: 1676-1682 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1555029420/2020©BEIESP | DOI: 10.35940/ijitee.D1555.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Recent years have seen the wide use of Time series forecasting (TSF) for predicting the future price stock, modeling and analyzing of finance time series helps in guiding the trades and investors decision. Moreover considering the stock as the dynamic environment, it is pronounced as the non-linearity of time series which affects the stock price forecast immediately. Hence, in this research work we propose intelligent TSF model, which helps in forecasting the early prediction of stock prices. The proposed stock price forecasting model employed both short-term (i.e. recent behavior fluctuation) using log bilinear (LBL) model and long-term (i.e., historical) behavior using recurrent neural network (RNN) based LSTM (long short term memory )model. Subsequently, this model is mainly helpful for the home brokers since they do not possess enough knowledge about the stock market. Proposed RNNLBL hybrid model shows the satisfying forecasting performance, these results in overall profit for the investors and trades. Furthermore, proposed model possesses a promising forecasting in case of the non-linear time series since the pattern of non-linear pattern are highly improbable to capture through these state-of-art stock price forecasting models.
Keywords:  Recurrent Neural Networks, Long Short term Memory, Log Bilinear Model, Machine learning, Prediction System, Stock Price Forecasting, Time Series.
Scope of the Article: Machine learning