A Hybrid Analysis of Stock Price Prediction using Vector Auto-Regressive (VAR), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU
Keywords:
.Abstract
Railway sector in India vast developing industry for which government of India creates thestocks IRFC and Railvikas to contributes the railway sector by make the investors invest inthese stocks. This study, forecast the future trends of these two stocks at the same time usingthe statistical method and the machine learning methods. The performance evaluation metricsMAE and MSE are used in the model to conform the model accuracy. In addition toproviding useful solutions for the issues faced by stock capitalists, accurate stock forecastingwould surely be extremely advantageous for stock exchange governments.
References
Shiri, F. M., Perumal, T., Mustapha, N., & Mohamed, R. (2023). A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU. arXiv preprint arXiv:2305.17473.
Cahuantzi, Roberto & Chen, Xinye&Güttel, Stefan. (2023). A Comparison of LSTM and GRU Networks for Learning Symbolic Sequences. 10.1007/978-3-031-37963-5_53.
