The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Time series forecasting with applications to finance
Author:	Misra, Viswapriya
View Online:	njit-etd2021-024 (xi, 65 pages ~ 7.0 MB pdf)
Department:	Department of Computer Science
Degree:	Master of Science
Program:	Data Science
Document Type:	Thesis
Advisory Committee:	Wang, Jason T. L. (Committee chair) Wang, Guiling (Committee member) Herbert, Katherine Grace (Committee member)
Date:	2021-05
Keywords:	Time series forcatesting Finance Stock price prediction
Availability:	Unrestricted
Abstract:	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.