Alghamdi, Dhaifallah
(2022)
A Deep Learning Based Approach for Time-series
Modeling.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
Big data has evolved as a new research domain in the digital era in which we live today. This domain deals with the study of huge datasets with numerous different features, whose volumes are rapidly snowballing with time. These types of datasets can be produced by different autonomous sources, including scientific experiments, engineering applications, government records, financial transactions, etc. The availability of big data is of a great value because of the opportunity this provides for making better-informed decisions, but it also requires advanced analytical tools to derive important insights for these decisions. This is the main reason that artificial intelligence (AI) and machine learning (ML) have gained immense popularity in recent years.
Time-series forecasting is an important application area for machine learning. It is important because there are so many prediction problems from various application domains that involve a time component. However, the temporal dimension also makes time-series problems more challenging to handle as opposed to many other prediction tasks. For this purpose, the goal of this dissertation is to design end-to-end frameworks and build advanced models for time-series forecasting that are based on deep learning. The discussed frameworks in this dissertation share three important characteristics: 1) the ability to generate forecasts for multiple steps ahead in the future, 2) the ability to provide estimates of uncertainty associated with these forecasts, and 3) the flexibility to incorporate exogenous factors. Our approach is to harness the encoder-decoder architecture to learn from historical data and capture important relationships embedded in the time-series, and to then use this knowledge to generate forecasts for multiple steps in the future along with estimates on the uncertainty associated with these forecasts.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
6 September 2022 |
| Date Type: |
Publication |
| Defense Date: |
24 May 2022 |
| Approval Date: |
6 September 2022 |
| Submission Date: |
16 June 2022 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
145 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Industrial Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Deep Learning, Time-series |
| Date Deposited: |
06 Sep 2022 16:12 |
| Last Modified: |
06 Sep 2023 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/43239 |
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