Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/15054
Title: A TFD Approach to Stock Price Prediction
Authors: Chanduka B.
Bhat S.S.
Rajput N.
Mohan B.R.
Issue Date: 2020
Citation: Advances in Intelligent Systems and Computing , Vol. 1034 , , p. 635 - 644
Abstract: Accurate stock price predictions can help investors take correct decisions about the selling/purchase of stocks. With improvements in data analysis and deep learning algorithms, a variety of approaches has been tried for predicting stock prices. In this paper, we deal with the prediction of stock prices for automobile companies using a novel TFD—Time Series, Financial Ratios, and Deep Learning approach. We then study the results over multiple activation functions for multiple companies and reinforce the viability of the proposed algorithm. © 2020, Springer Nature Singapore Pte Ltd.
URI: https://doi.org/10.1007/978-981-15-1084-7_61
http://idr.nitk.ac.in/jspui/handle/123456789/15054
Appears in Collections:2. Conference Papers

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