A TFD Approach to Stock Price Prediction

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DC Field	Value	Language
dc.contributor.author	Chanduka B.
dc.contributor.author	Bhat S.S.
dc.contributor.author	Rajput N.
dc.contributor.author	Mohan B.R.
dc.date.accessioned	2021-05-05T10:16:18Z	-
dc.date.available	2021-05-05T10:16:18Z	-
dc.date.issued	2020
dc.identifier.citation	Advances in Intelligent Systems and Computing , Vol. 1034 , , p. 635 - 644	en_US
dc.identifier.uri	https://doi.org/10.1007/978-981-15-1084-7_61
dc.identifier.uri	http://idr.nitk.ac.in/jspui/handle/123456789/15054	-
dc.description.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.	en_US
dc.title	A TFD Approach to Stock Price Prediction	en_US
dc.type	Conference Paper	en_US
Appears in Collections:	2. Conference Papers

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