2017 2nd IEEE International Conference on Recent Trends in Electronics, Information &Amp; Communication Technology (RTEICT) 2017
DOI: 10.1109/rteict.2017.8256643
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Short term stock price prediction using deep learning

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Cited by 90 publications
(36 citation statements)
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“…There were also multiple and hybrid models that used mostly technical analysis features as their inputs to the DL model. Several technical indicators were fed into LSTM and MLP networks in [105] for predicting intraday price prediction. Recently, Zhou et.…”
Section: Stock Price Forecastingmentioning
confidence: 99%
“…There were also multiple and hybrid models that used mostly technical analysis features as their inputs to the DL model. Several technical indicators were fed into LSTM and MLP networks in [105] for predicting intraday price prediction. Recently, Zhou et.…”
Section: Stock Price Forecastingmentioning
confidence: 99%
“…Regarding the works that implemented the LSTM technique, which are more than half of the analysed publications, some approached the pre-processing, results comparisons, and accuracy metrics in similar ways. The authors by [40], [43], [44], [47], [53] used asset prices and TIs as network attributes, and the data were normalized to feed the model input based on the LSTM network. Among them, only Qiu et al [53] proposed a new model: a combination of LSTM and GRU (Gated Recurrent Unit), to explore LSTM ability to process sequential data and the simplicity of GRU, reducing training time and computational cost.…”
Section: ) Analysis Based On Predictor Techniquesmentioning
confidence: 99%
“…Regarding the gaps and future work proposed by the articles explored, the most cited are related to implementing an algotrading with trading strategy [8], [36], [38], [43], [53], [58], [60]- [62]. It shows that the authors realize the importance of model validation through a simulated or real environment.…”
Section: Number Of Publicationsmentioning
confidence: 99%
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“…Email: xyz3@blueeyesintlligence.org intelligence only had be in the area of mathematics, but its development has exploded with the introduction of artificial neural networks such as TensorFlow actively supported by Google, Caffe created by Facebook, and Torch, an open source project library. Libraries made easy to use have led to the universalization of artificial intelligence, while lowering the barriers to entry into AI development [9][10].…”
Section: Revised Manuscript Received On July 22 2019mentioning
confidence: 99%