Comparative Study of Predicting Stock Index Using Deep Learning Models
Harshal Patel Bharath Kumar Bolla Sabeesh E Dinesh Reddy
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.