Full metadata
Title
Time series prediction for stock price and opioid incident location
Description
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 Memory (LSTMs) models.
In this thesis, the Opioid Incident Prediction Problem investigates methods which could predict the location of future opioid overdose incidences using the past opioid overdose incidences data. A similar deep learning model is used to predict the location of the future overdose incidences given the two datasets of the past incidences (Connecticut and Cincinnati Opioid incidence datasets) and compared with the existing neural network models such as Convolution LSTMs, Attention-based Convolution LSTMs, and Encoder-Decoder frameworks. Experimental results on the above-mentioned datasets for both the problems show the superiority of the proposed architectures over the standard statistical models.
In this thesis, the Opioid Incident Prediction Problem investigates methods which could predict the location of future opioid overdose incidences using the past opioid overdose incidences data. A similar deep learning model is used to predict the location of the future overdose incidences given the two datasets of the past incidences (Connecticut and Cincinnati Opioid incidence datasets) and compared with the existing neural network models such as Convolution LSTMs, Attention-based Convolution LSTMs, and Encoder-Decoder frameworks. Experimental results on the above-mentioned datasets for both the problems show the superiority of the proposed architectures over the standard statistical models.
Date Created
2019
Contributors
- Thomas, Kevin, M.S (Author)
- Sen, Arunabha (Thesis advisor)
- Davulcu, Hasan (Committee member)
- Banerjee, Ayan (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
viii, 73 pages : illustrations, map
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.54930
Statement of Responsibility
by Kevin Thomas
Description Source
Viewed on August 25, 2020
Level of coding
full
Note
thesis
Partial requirement for: M.S., Arizona State University, 2019
bibliography
Includes bibliographical references
Field of study: Computer science
System Created
- 2019-11-06 03:40:12
System Modified
- 2021-08-26 09:47:01
- 3 years ago
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