A TFD Approach to Stock Price Prediction

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dc.contributor.authorChanduka B.
dc.contributor.authorBhat S.S.
dc.contributor.authorRajput N.
dc.contributor.authorMohan B.R.
dc.date.accessioned2021-05-05T10:16:18Z
dc.date.available2021-05-05T10:16:18Z
dc.date.issued2020
dc.description.abstractAccurate 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.identifier.citationAdvances in Intelligent Systems and Computing , Vol. 1034 , , p. 635 - 644en_US
dc.identifier.urihttps://doi.org/10.1007/978-981-15-1084-7_61
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/15054
dc.titleA TFD Approach to Stock Price Predictionen_US
dc.typeConference Paperen_US

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