Document Type


Date of Award


Degree Name

Master of Science in Data Science - (M.S.)


Computer Science

First Advisor

Jason T. L. Wang

Second Advisor

Guiling Wang

Third Advisor

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.

Included in

Data Science Commons



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