Monitoring and Training Stock Prediction System For Historical & Live Dataset using Lstm & Cnn
Omveer Singh Deora1, Pawan Jha2, S.T. Sawant Patil3, T.B. Patil4, S. D. Joshi5

1Omveer Singh Deora, Research Scholar Department of Information Technology Bharati Vidyapeeth (Deemed to be University), College of Engineering, Pune, Maharashtra, India
2Pawan Jha, Research Scholars Department of Information Technology, Bharati Vidyapeeth (Deemed to be University), College of Engineering, Pune, Maharashtra, India
3Prof. S.T. Sawant Patil, Assistant Professor Department of Information Technology, Bharati Vidyapeeth (Deemed to be University), College of Engineering, Pune, Maharashtra, India
4Prof. T.B. Patil, Assistant Professor Department of Information Technology, Bharati Vidyapeeth (Deemed to be University), College of Engineering, Pune, Maharashtra, India
5Dr. S. D. Joshi, Professor Department of Information Technology, Bharati Vidyapeeth (Deemed to be University), College of Engineering, Pune, Maharashtra, India

Manuscript received on 28 August 2019. | Revised Manuscript received on 04 September 2019. | Manuscript published on 30 September 2019. | PP: 1636-1639 | Volume-8 Issue-11, September 2019. | Retrieval Number: K15910981119/2019©BEIESP | DOI: 10.35940/ijitee.K1591.0981119
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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: A country development and stability are directly associated with its economy and today’s economy is profoundly dependent on the Stock market. Stock market indexes are subject to continuous change with respect to time, a hype or fall in the stock market has a crucial role in deciding the investor’s profit. Due to the economical ups & down and rapid growth in profit from the stock market, there required a need of developing a software application which continuously monitor the stock index’s and a prediction algorithm which can predict the possible change in stock index as for where it can go in future. Prediction of stock market does not follow any rules or predefined guidelines, hence prediction of stock market is difficult to achieve and the data-set for stock market prediction is also non-linear in nature which requires an efficient approach to resolve the time-series dependency of non-linear data. In our proposed system we are using the LSTM (long short-term memory) for efficiently predicting the stock index on historical data and the sudden change in stock market due to number of un-controllable factors is analysed by CNN model. As per the noise in the data-set we are employing wavelet denoising technique. If any changes in stock index with more than 10% of its initial value is analysed by monitoring module, then the system will notify the user with the change and also aggregating the result of predicting algorithm on that specific stock. Using our model Moneypred the accuracy in stock prediction is more than 70%.
Keywords: Monitoring of stock market; Prediction of stock market; RNN (recurrent neural network); LSTM (long short-term memory neural network); CNN (convolutional neural network); Denoising of data using wavelet transform.
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