Longitudinal Data Analysis and Time Series Commons^™

Changepoint Detection As Model Selection: A General Framework, Michael A. Grantham

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation presents a general framework for changepoint detection based on ℓ₀ model selection. The core method, Iteratively Reweighted Fused Lasso (IRFL), improves upon the generalized lasso by adaptively reweighting penalties to enhance support recovery and minimize criteria such as the Bayesian Information Criterion (BIC). The approach allows for flexible modeling of seasonal patterns, linear and quadratic trends, and autoregressive dependence in the presence of changepoints.

Simulation studies demonstrate that IRFL achieves accurate changepoint detection across a wide range of challenging scenarios, including those involving nuisance factors such as trends, seasonal patterns, and serially correlated errors. The framework is …

Postprocessing Gan-Generated Synthetic Time Series Using Dynamic Time Warping, Md Raisul Islam Roni

Electronic Theses and Dissertations

Generative Adversarial Networks (GANs) are a class of deep learning models capable of producing realistic synthetic data that preserve the statistical and temporal characteristics of real datasets. The DoppelGANger (DGAN) framework extends this approach to time series data by jointly modeling temporal dependencies and contextual metadata. However, synthetic sequences generated by GAN may show temporal misalignment, resulting in inconsistencies when compared with real data. This study presents a postprocessing framework based on Dynamic Time Warping (DTW) and its differentiable extension Soft-DTW to improve the temporal alignment of synthetic time series. The framework is evaluated using quantitative measures of alignment and …

A Leslie System For A Demographic Simulation: From An Actuarial Point Of View, David Kings

Electronic Theses and Dissertations

This thesis develops a discrete stochastic linear systems interpretation of age–stage demographic evolution grounded in Leslie operators and realized in a discrete-event simulation implemented with salabim. The central claim is that one annual cycle of the simulation constitutes a cone-preserving, stochastic affine transformation on a high- dimensional population state vector indexed by age, sex, marital status, household type, employment, and education, and that the composition of yearly operators yields a random matrix product whose top Lyapunov exponent is the stochastic counterpart of the Perron–Frobenius growth rate (Caswell, 2001; Tuljapurkar, 1997)[1, 2]. The actuarial bridge is constructed by mapping simulated survival …

Latent Classification Of Time-Dependent Transition Rates In Longitudinal Binary Outcome Data, Joonha Chang, Wenyaw Chan

School of Public Health Faculty Publications

Continuous-time Markov chain (CTMC) models and latent classification methods are commonly used to analyze longitudinal categorical outcomes in medical research. While CTMC models are popular for their simplicity and effectiveness, their assumption of constant transition rates presents limitations in capturing dynamic behaviors. To address this, non-homogeneous continuous-time Markov chains (NH-CTMCs) have been developed, incorporating time-varying transition rates to enhance model flexibility. In this study, we leverage closed-form transition probabilities for a fully ergodic two-state NH-CTMC model and propose a latent class clustering approach to identify heterogeneous transition rate patterns within the population. We emphasize the potential advantages of these models …

Evaluating Alpha Spending Functions Applied To Observational Time-To-Event Analysis, Moses Torgbenu

Electronic Theses and Dissertations

This thesis explores the theoretical foundation of the alpha spending approach and extends its application beyond the conventional setting of randomized controlled trials (RCTs) to observational studies with time to event analyses. In these less structured environments, key design parameters such as the total number of events are often unknown, posing challenges for the standard implementation of sequential analysis methods.

Through simulation studies, this research delivers several important contributions. First, it presents a modified approach that uses calendar time to define the timing of interim analyses while relying on event-based information to estimate the correlation among test statistics. This adjustment …

A Data-Driven Approach To Time Series Forecasting And Clustering Of U.S. Regional Drug Overdose Mortality, Koshali Hamy Muthunama Gonnage

Mathematics & Statistics ETDs

The increasing rate of drug overdose deaths in the United States poses a critical public health challenge, particularly due to the surge in synthetic opioids and other high-risk substances. This study presents a data-driven framework that integrates time series forecasting and clustering techniques. Monthly mortality data for five key drug types: cocaine, fentanyl, heroin, methamphetamine, and oxycodone were analyzed using four time series forecasting models: ARIMA, ETS, TBATS, and NNAR. These models were evaluated using standard accuracy metrics RMSE, MAPE, and MAE to assess predictive performance. Signal decomposition approach based on Singular Value Decomposition and subspace modeling was employed to …

The Nature Of Anthropogenically Driven River Drying: Spatiotemporal Causes And Consequences, Eliza Inez Gilbert

Biology ETDs

Streambed drying naturally occurs in over 60% of rivers and streams worldwide. Climate change and human regulation of surface and groundwater have increased drying in naturally intermittent systems and caused perennial systems to transition to intermittency, impacting water security, water quality, and biodiversity. To understand human-induced drying dynamics, we used 12 years of daily drying data along a 154-km regulated reach of the Rio Grande. We conceptualized river drying as a regime analogous to the natural flow regime paradigm and quantified drying magnitude, rate of change, and duration. Although linear models predicting drying magnitude and rate of change were uninterpretable, …

Conceptualizing The Explanatory Fully Longitudinal Mixed Methods Case Study Design: A Demonstration With An Arithmetic Education Trial With Kindergarten Children, Menglong Cong

Electronic Theses and Dissertations

In employing longitudinal mixed methods designs, researchers have commonly used the fully longitudinal mixed methods design. This has occurred mostly in the health sciences but less in education. The current investigation proposes a novel longitudinal mixed methods research design, explanatory fully longitudinal mixed methods case study design. It demonstrates its potential for addressing research inquiries in educational research using the arithmetic learning trajectories datasets. This study is presented in three components. The first is a quantitative phase, selecting an exploratory case from a previous arithmetic learning trajectories study (Clements et al., 2021) for qualitative analyses based on maximizing the …

Bandwagon Behavior In Major League Baseball, Daniel E. Erro

Master's Theses

This study investigates “bandwagon” behavior among Major League Baseball (MLB) fans by analyzing Google search interest data from 2004 to 2019. Drawing on publicly available information from Google Trends, the analysis explores how fluctuations in search activity align with team performance during both the regular season and postseason. Hierarchical linear models are used to estimate expected levels of fan interest based on team performance and market characteristics. Deviations from these expectations during the regular season are interpreted as evidence of bandwagon or anti-bandwagon behavior. A drop-off in interest following playoff elimination is also examined to capture shifts in fan attention …

Hybrid Graph-Recurrent Architecture For Citation Recommendation Via Future Embedding Forecasting, Mohammad Ausaf Ali Haqqani

Computer Science and Engineering Theses and Dissertations

The rapid expansion of scientific literature has intensified the challenge of identifying relevant citations, particularly for newly published or under-cited papers. Traditional citation recommendation systems typically model static relationships or respond to past citation activity, offering limited predictive power for emerging works. In response, this thesis presents a temporal modeling framework for citation recommendation that anticipates future scholarly relevance by forecasting the latent representations of academic papers.

Building on prior work that utilized Temporal Graph Networks (TGNs) to model dynamic citation flows, we propose Graph-Time, a hybrid architecture that integrates a Graph Transformer with a GRU-based time series predictor. The …

Using A Pharmacokinetic Model To Design And Evaluate An Early Ctdna Biomarker For Response To Targeted Therapy, Aaron Li

Spora: A Journal of Biomathematics

Early prediction of response to therapy or lack thereof can help physicians plan treatment more efficiently. Biomarkers based on circulating tumor DNA (ctDNA) are promising. However, biomarkers beyond direct comparison to baseline have not been thoroughly explored. We develop a model for ctDNA shedding under targeted therapy that incorporates pharmacokinetics. Using a simulated cohort of virtual patients with varied parameters, we define and analyze a biomarker based on ctDNA samples at baseline, 12 hours, and 24 hours after initiation of treatment. The biomarker identified patients who would achieve partial or complete response with high sensitivity and specificity and was able …

New Bayesian Methods For Longitudinal Data Analysis With Complex Dependence Structures., Anish Mukherjee

Electronic Theses and Dissertations

Longitudinal data in real-world settings are frequently found to be heterogeneous and exhibit intricate spatio-temporal dependence structures. Analyzing such complex data to obtain reliable estimation while quantifying uncertainty necessitates using sophisticated Bayesian methodology. In this work, we present novel Bayesian methods developed to address these challenges. We often observe heterogeneity in longitudinal data, where the mean and variance for certain profiles meaningfully differs from the rest. Some profiles may also exhibit outliers at a limited number of measurements. Using a standard mixed effects model, which assumes homogeneity, can lead to overestimating the residual variance and inefficient estimation. In this work, …

Enhancing Animal Shelter Operations With Time Series And Machine Learning, Sakava L. Kiv, Donald L. Anderson, Shivam Negi, Jacquelyn Cheun

SMU Data Science Review

Enhancing animal shelter operations through machine learning involves employing a variety of advanced techniques aimed at increasing efficiency, promoting animal welfare, and optimizing resource allocation. This paper explores predictive analytics for adoption rates using regression models to estimate the likelihood of adoption based on historical data, encompassing variables such as breed, health status, and previous adoption trends. Additionally, classification algorithms are utilized to categorize animals by adoption probability, facilitating better resources and marketing prioritization. Clustering algorithms are employed to group animals according to behavior patterns and/or physical health, enabling tailored medical care and enrichment activities that improve their mental and …

A Machine-Learning Tool-Supported Methodology For Nonprofit Donor Analysis, Corbin Weiss

Campus Research Month

We developed a machine-learning tool-supported methodology for modeling the nonprofit donor relationship. This approach was demonstrated in the case of a US-based nonprofit. Conclusions were drawn from this example and tool-support provided for use by other nonprofits.

Urban Heat Dynamics In Pune: The Influence Of Land Cover And Local Climate, Arpit Tiwari, Preethi Nanjundan, Ravi Ranjan Kumar, Ananya Karmakar, Satyaban Bishoyi Ratna

Northeast Journal of Complex Systems (NEJCS)

Urban areas with high population density and extensive infrastructure development have been experiencing an increasing strain on the local heat budget, leading to a surge in heat-related illnesses and discomfort. This study examined the impact of climate and land use as heat islands in Pune, India, from 2012 to 2023 at six different locations representing varying degree of urbanization. Satellite land cover observations revealed that 55.17% of the total area was urbanized in the city itself, which was limited to 44.8% in 2012. This urbanization has significantly impacted the increasing tendency of maximum temperature (T_max; 0.13℃ to 1.63℃ …

Filters For Forecasting Crop Health: Analyzing And Projecting The Temporal Evolution Of Landsat Ndvi Data Using Dynamic Linear Models And The Kalman Filter, Kamal Albousafi, Hossein Moradi, Jung-Han Kimn

SDSU Data Science Symposium

Accurately forecasting food availability is a critical task. One approach involves utilizing remote sensing data, such as satellite images, to observe the health of crop fields using different Vegetation Indices (VI). The Normalized Difference Vegetation Index (NDVI) provides a sound metric to track the “greenness” of crops over time. In this research, we develop statistical models that capture the dynamics of NDVI time series data to make better predictions of its future values. The median NDVI of the pixels of a farm located in Edmunds County, South Dakota, is obtained using imagery from the Landsat 5 and Landsat 8 satellites, …

Beyond Homogeneity: Exploring Causal Heterogeneity In Psychopathology, Philip B. Vinh

Theses and Dissertations

Traditional models in psychiatric research often impose assumptions of causal homogeneity, treating population-level associations as reflective of uniform underlying mechanisms. This dissertation challenges that assumption by introducing statistical and machine learning frameworks designed to detect and model causal heterogeneity in the development of psychopathology. Central to this approach is the advancement of finite mixture structural equation modeling (FM-SEM) to identify latent subgroups characterized by distinct, and sometimes opposing, causal pathways.

The dissertation comprises three integrated empirical studies. The first introduces mixDoC, a finite mixture extension of the classical Direction of Causation (DoC) model applied to twin data, enabling the detection …

Crime Modeling Using An Integrated Cnn–Lstm Architecture With Embedded Self-Excitation, Pawandeep Kaur

Theses and Dissertations (Comprehensive)

It is often assumed that natural phenomena occur randomly over time. However, careful analysis reveals that these events typically form some series or sequences and exhibit distinctive temporal patterns. These patterns are not exclusive to nature. They also appear in human activities, often studied under the concept of bursty human dynamics. The statistical methods analyzing bursty human dynamics not only capture overall trends or seasonality but also explore how past events influence future ones. It makes the analysis more realistic and the results more closely aligned with reality. Bursty human dynamics can be studied at two levels: the individual level …

A Time Series Analysis Of The Macroeconomic Indicators, Mia Houston

Honors Undergraduate Theses

Understanding inflation—particularly across regions and categories—is crucial for effective policymaking, strategic business decisions, and safeguarding vulnerable populations, as it highlights the diverse drivers and impacts of price changes within the economy. This has become increasingly crucial in recent years between the volatile inflation conditions introduced by the COVID-19 pandemic, energy price shocks, and renewed trade tensions and tariffs. This thesis analyzes 77 U.S. monthly inflation time series from 2003 to 2023 using two forecasting approaches: an elementwise Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a Factor-Augmented Vector Autoregressive (FAVAR) model. The data obtained from the Bureau of Labor Statistics …

Time Series Modeling Of Akron Air Quality Index (Aqi) Data, Mason Yurich

Williams Honors College, Honors Research Projects

With the increase in population and industrialization around the world, climate has become a major concern for many researchers. One measure that has drawn much interest is air quality. There are available resources that track the Air Quality Index (AQI) in most large cities, but there is a general lack of information regarding Air Quality forecasts, even for one day in the future. This project aims to find a useful statistical model for representing and predicting the AQI measure in Akron, Ohio over time. By using historical air quality data from the United States Environmental Protection Agency and AQI.in, an …