Date of Award
Master of Science in Data Science - (M.S.)
Jason T. L. Wang
Katherine Grace Herbert
In finance, many phenomena are modeled as time series. This thesis investigates time series forecasting problems in finance, precisely the stock price prediction problem. We employ and compare traditional statistical algorithms like MA, ARIMA, and ARMA-GARCH with newly developed deep learning-based algorithms such RNNs, LSTMs, GRUs, TCNs, and bidirectional LSTMs and GRUs for predicting stock prices. We perform a comprehensive study and present all the experimental results on different datasets. We find that ARIMA and GRU perform better for single-step stock price prediction than other deep learning architectures. Adding market and economic indicators do not improve the performance of the deep learning models. In the case of multistep forecasting, ARIMA outperforms multistep GRU/TCN and Seq2Seq GRU/TCN. Also, transfer learning helps to improve the performance of the deep learning models.
Misra, Viswapriya, "Time series forecasting with applications to finance" (2021). Theses. 1836.