A Comparative Analysis of ARIMA, GRU, LSTM and BiLSTM on Financial Time Series Forecasting-Reference-Cited by-同舟云学术

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A Comparative Analysis of ARIMA, GRU, LSTM and BiLSTM on Financial Time Series Forecasting

Published:2022-04-23 Issue: Volume: Page:
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Container-title:2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)
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Author:

Pirani Muskaan¹,Thakkar Paurav²,Jivrani Pranay²,Bohara Mohammed Husain¹,Garg Dweepna¹

Affiliation:

1. Charotar University of Science and Technology (CHARUSAT),Devang Patel Institute of Advance Technology and Research (DEPSTAR),Anand,Gujarat,India

2. Charotar University of Science and Technology(CHARUSAT),Chandubhai S Patel Institute of Technology (CSPIT),Anand,Gujarat,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9792621/9792636/09793213.pdf?arnumber=9793213

Reference22 articles.

1. Variations on rolling forecasts;hyndman,2014

2. Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore

3. Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500

4. A deep efficient frontier method for optimal investments;lee,2017

5. Deep learning with long short-term memory networks for financial market predictions

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