Keywords

Time Series Modeling, Deep Learning, Data Mining

Abstract

Time series data, as a pervasive kind of data format, have played one key role in numerous realworld scenarios. Effective time series modeling can help with accurate forecasting, resource optimization, risk management, etc. Considering its great importance, how can we model the nature of the pervasive time series data? Existing works have used adopted statistics analysis, state space models, Bayesian models, or other machine learning models for time series modeling. However, these methods usually follow certain assumptions and don't reveal the core and underlying rules of time series. Moreover, the recent advancement of deep learning has made neural networks a powerful tool for pattern recognition. This dissertation will target the problem of time series modeling using deep learning techniques to achieve accurate forecasting of time series. I will propose a principled approach for deep time series modeling from a novel distribution perspective. After in-depth exploration, I categorize and study two essential characteristics of time series, i.e., the distribution regularity and the distribution shift, respectively. I will investigate how can time series data involving the two characteristics be analyzed by distribution extraction, distribution scaling, and distribution transformation. By applying more recent deep learning techniques to distribution learning for time series, this defense aims to achieve more effective and efficient forecasting and decision-making. I will carefully illustrate of proposed methods of three themes and summarize the key findings and improvements achieved through experiments. Finally, I will present my future research plan and discuss how to broaden my research of deep time series modeling into a more general Data-Centric AI system for more generalized, reliable, fair, effective, and efficient decision-making.

Completion Date

2023

Semester

Fall

Committee Chair

Fu, Yanjie

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Format

application/pdf

Identifier

DP0028027

URL

https://purls.library.ucf.edu/go/DP0028027

Language

English

Release Date

December 2026

Length of Campus-only Access

3 years

Access Status

Doctoral Dissertation (Campus-only Access)

Campus Location

Orlando (Main) Campus

Download

Restricted to the UCF community until December 2026; it will then be open access.

Share

COinS