NSE Stock Prediction: The Deep Learning Way

Barai, Ankit Khushal; Jain, Pooja; Kumar, Tapan

doi:10.1007/978-981-15-4032-5_70

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Various researchers have also used other models such as DNN, ANN, RNN, LSTM, Random Forest, MLP, Support Vector Regression etc. to predict the movement of market with great accuracy [10] [24]. It has been seen that varying the value of these parameters shows significant variation in the prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It has been seen that varying the value of these parameters shows significant variation in the prediction. In one research it was also discovered that employing a model methodology to choose a neural network model that would precisely forecast the value of stocks yielded better results than using the ARIMA model [10]. It is evident from various observations that suitable neural network selection for different stocks can vary significantly.…”

Section: Literature Reviewmentioning

confidence: 99%

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Designing an Interface to Forecast Stock Close Price Using ARIMA

Dixit¹,

Ahirwar²,

Saketnath³

et al. 2023

Artificial Intelligence and Communication Technologies

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Stock market prediction involves a great effect of the current market trend on the price. It is thus necessary to create a model that efficiently identifies the market trend and predicts the values with high consistency and accuracy. This paper focuses on designing a system that uses the ARIMA model to predict the values. The python flask API has been used to design the web application and tkinter API has been used to design the GUI interface. To fetch the live market data nse2r library of R and nsetools library of python has been used to fetch the past market data for a period of six years.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%