Predicting Stock Market Prices using Fine-Tuned Ind RNN
Ahmad Alsharef1, Prachet Bhuyan2, Abhishek Ray3
1Ahmad Alsharef*, School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University/ Bhubaneswar, India.
2Prachet Bhuyan, School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University/ Bhubaneswar, India.
3Abhishek Ray, School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University/ Bhubaneswar, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on May 10, 2020. | PP: 309-315 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5237059720/2020©BEIESP | DOI: 10.35940/ijitee.G5237.059720
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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: Prediction and analysis of stock market data have a vital role in current time’s economy. The various methods used for the prediction can be classified into 1) Linear Algorithms like Moving Average (MA) and Auto-Regressive Integrated Moving Average (ARIMA). 2) Non-Linear Models like Artificial Neural Networks and Deep Learning. In this work, we are using the results of previous research papers to demonstrate the potential of some models like ARIMA, Multi-Layer Perception (MLP) ), Convolutional Neural Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long-Short Term Memory (LSTM) for forecasting the stock price of an organization based on its available historical data. Then, implementing some of these methods to check and compare their efficiency within the same issue. We used Independently RNN (IndRNN) to explore a better efficiency for stock prediction and we found that it gives better accuracy prevailing methods in the current time. We also proposed an enhancement to IndRNN by replacing its default activation function with a more effective function called Parametric Rectified Linear Unit (PreLU). Our proposed approach can be used as an alternative method for predicting time series data efficiently other than the typical approaches today.
Keywords: Deep learning, Independently Recurrent Neural Network, Parametric rectified linear unit, Stock prediction, Time-series forecasting.
Scope of the Article: Deep learning