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Time Series Prediction for Stock Price and Opioid Incident Location

Abstract Time series forecasting is the prediction of future data after analyzing the past data for temporal trends. This work investigates two fields of time series forecasting in the form of Stock Data Prediction and the Opioid Incident Prediction. In this thesis, the Stock Data Prediction Problem investigates methods which could predict the trends in the NYSE and NASDAQ stock markets for ten different companies, nine of which are part of the Dow Jones Industrial Average (DJIA). A novel deep learning model which uses a Generative Adversarial Network (GAN) is used to predict future data and the results are compared with the existing regression techniques like Linear, Huber, and Ridge regression and neural network models such as Long-Short Term Memo... (more)
Created Date 2019
Contributor Thomas, Kevin (Author) / Sen, Arunabha (Advisor) / Davulcu, Hasan (Committee member) / Banerjee, Ayan (Committee member) / Arizona State University (Publisher)
Subject Computer science / Generative Adversarial Network / Machine Learning / Opioid Incident Location Prediction / Stock Price Prediction
Type Masters Thesis
Extent 82 pages
Language English
Note Masters Thesis Computer Science 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis