ProgramsView all programs
From the news
- Chancellor Amma Addresses the Parliament of World’s Religions
- Amrita Students Qualify for the European Mars Rover Challenge
Publication Type : Conference Paper
Publisher : 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Source : 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2017)
Keywords : CNN, Companies, Data models, deep learning architectures, Economic forecasting, equity market, Forecasting, forecasting methods, investor, learning (artificial intelligence), Logic gates, LSTM, Machine learning, Neural networks, nonlinear algorithms, Predictive models, recurrent neural nets, RNN, share price, share prices, sliding window approach, stock index movement, Stock market, stock markets, Stock price prediction, Time series, Time series analysis
Campus : Coimbatore
School : School of Engineering
Center : Automotive Center, Computational Engineering and Networking
Department : Mechanical Engineering, Computer Science, Electronics and Communication
Year : 2017
Abstract : Stock market or equity market have a profound impact in today's economy. A rise or fall in the share price has an important role in determining the investor's gain. The existing forecasting methods make use of both linear (AR, MA, ARIMA) and non-linear algorithms (ARCH, GARCH, Neural Networks), but they focus on predicting the stock index movement or price forecasting for a single company using the daily closing price. The proposed method is a model independent approach. Here we are not fitting the data to a specific model, rather we are identifying the latent dynamics existing in the data using deep learning architectures. In this work we use three different deep learning architectures for the price prediction of NSE listed companies and compares their performance. We are applying a sliding window approach for predicting future values on a short term basis. The performance of the models were quantified using percentage error.
Cite this Research Publication : Sreelekshmy, S., Vinayakumar, R., Gopalakrishnan, E. A., Vijay, K. M. and Soman, K. P. “Stock price prediction using LSTM, RNN and CNN-sliding window model”. International Conference on Advances in Computing, Communications and Informatics, September 13-16, 2017, Manipal, India.