A TFD Approach to Stock Price Prediction

Simple item page
dc.contributor.authorChanduka, B.
dc.contributor.authorBhat, S.S.
dc.contributor.authorRajput, N.
dc.contributor.authorMohan, B.R.
dc.date.accessioned2026-02-06T06:37:06Z
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.
dc.identifier.citationAdvances in Intelligent Systems and Computing, 2020, Vol.1034, , p. 635-644
dc.identifier.issn21945357
dc.identifier.urihttps://doi.org/10.1007/978-981-15-1084-7_61
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/30868
dc.publisherSpringer
dc.subjectDeep learning
dc.subjectFinancial ratios
dc.subjectStock price prediction
dc.subjectTime series
dc.titleA TFD Approach to Stock Price Prediction

Files

Collections

Conference Papers