Prediction of stock price with enhanced deep learning model
Bhakirathi B
, M. Moogambikai
Prediction of the stock price and in specific the trend the stock price will follow is an important task in various perspectives. Recurrent neural networks are used for this task. Experimental analyses were made with different time stepss and different number of Long Short Term Memory (LSTM) layers. The dataset that has been used is the Google stock price of the past five years. It is a time series data which contains stock details of 1274 days. The performance parameters that are used for evaluating the model are explained variance score, r2score and the pearson coefficient . The results show the comparative performance of the recurrent neural network with different number of time steps and different number of LSTM layers. It has been observed that the model with 60 Time steps and 4 LSTM Layer performs better.
"Prediction of stock price with enhanced deep learning model ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 9, page no.345 - 353, September-2020, Available :https://ijsdr.org/papers/IJSDR2009055.pdf
Volume 5
Issue 9,
September-2020
Pages : 345 - 353
Paper Reg. ID: IJSDR_192484
Published Paper Id: IJSDR2009055
Downloads: 000347214
Research Area: Engineering
Country: Theni, Tamilnadu , India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
