Research Article
Comparative Study of Predicting Stock Index Using Deep Learning Models
@INPROCEEDINGS{10.1007/978-3-031-48888-7_4, author={Harshal Patil and Bharath Kumar Bolla and E. Sabeesh and Dinesh Reddy Bhumireddy}, title={Comparative Study of Predicting Stock Index Using Deep Learning Models}, proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I}, proceedings_a={IC4S}, year={2024}, month={1}, keywords={ARIMA SARIMA SARIMAX RNN CNN LSTM GRU DeepAR DSSM DF-RNN Deep Renewal POCID Thiels’U}, doi={10.1007/978-3-031-48888-7_4} }- Harshal Patil
Bharath Kumar Bolla
E. Sabeesh
Dinesh Reddy Bhumireddy
Year: 2024
Comparative Study of Predicting Stock Index Using Deep Learning Models
IC4S
Springer
DOI: 10.1007/978-3-031-48888-7_4
Abstract
Time series forecasting has seen many methods attempted over the past few decades, including traditional technical analysis, algorithmic statistical models, and more recent machine learning and artificial intelligence approaches. Recently, neural networks have been incorporated into the forecasting scenario, such as the LSTM and conventional RNN approaches, which utilize short-term and long-term dependencies. This study evaluates traditional forecasting methods, such as ARIMA, SARIMA, and SARIMAX, and newer neural network approaches, such as DF-RNN, DSSM, and Deep AR, built using RNNs. The standard NIFTY-50 dataset from Kaggle is used to assess these models using metrics such as MSE, RMSE, MAPE, POCID, and Theil’s U. Results show that Deep AR outperformed all other conventional deep learning and traditional approaches, with the lowest MAPE of 0.01 and RMSE of 189. Additionally, the performance of Deep AR and GRU did not degrade when the amount of training data was reduced, suggesting that these models may not require a large amount of data to achieve consistent and reliable performance. The study demonstrates that incorporating deep learning approaches in a forecasting scenario significantly outperforms conventional approaches and can handle complex datasets, with potential applications in various domains, such as weather predictions and other time series applications in a real-world scenario.
