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dc.contributor.authorMaiti A.
dc.contributor.authorShetty D P.
dc.date.accessioned2021-05-05T10:15:55Z-
dc.date.available2021-05-05T10:15:55Z-
dc.date.issued2020
dc.identifier.citationIEEE Region 10 Annual International Conference, Proceedings/TENCON , Vol. 2020-November , , p. 1215 - 1220en_US
dc.identifier.urihttps://doi.org/10.1109/TENCON50793.2020.9293712
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/14879-
dc.description.abstractIn this paper, we predict the stock prices of five companies listed on India's National Stock Exchange (NSE) using two models- the Long Short Term Memory (LSTM) model and the Generative Adversarial Network (GAN) model with LSTM as the generator and a simple dense neural network as the discriminant. Both models take the online published historical stock-price data as input and produce the prediction of the closing price for the next trading day. To emulate the thought process of a real trader, our implementation applies the technique of rolling segmentation for the partition of training and testing dataset to examine the effect of different interval partitions on the prediction performance. © 2020 IEEE.en_US
dc.titleIndian stock market prediction using deep learningen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

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