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In this paper, we propose a novel deep neural network DP-LSTM for stock price prediction, which incorporates the news articles as hidden information and integrates difference news sources through the differential privacy mechanism.
Ranked #1 on Stock Price Prediction on 2019_test set
Based on the data of 2015 to 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week.
This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment.
Then the future stock prices are predicted as a nonlinear mapping of the combination of these components in an Inverse Fourier Transform (IFT) fashion.
Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis.
This paper addresses the estimation of the latent dimensionality in nonnegative matrix factorization (NMF) with the \beta-divergence.