Open Access. Powered by Scholars. Published by Universities.®
Longitudinal Data Analysis and Time Series Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Statistical Models (7)
- Computer Sciences (5)
- Data Science (5)
- Medicine and Health Sciences (5)
- Social and Behavioral Sciences (5)
-
- Categorical Data Analysis (4)
- Applied Statistics (3)
- Engineering (3)
- Analysis (2)
- Biostatistics (2)
- Business (2)
- Education (2)
- Educational Administration and Supervision (2)
- Educational Assessment, Evaluation, and Research (2)
- Electrical and Computer Engineering (2)
- Epidemiology (2)
- Graphics and Human Computer Interfaces (2)
- Life Sciences (2)
- Mathematics (2)
- Mental and Social Health (2)
- Numerical Analysis and Scientific Computing (2)
- Other Statistics and Probability (2)
- Probability (2)
- Public Health (2)
- Vital and Health Statistics (2)
- American Politics (1)
- Applied Behavior Analysis (1)
- Institution
- Keyword
-
- Epidemiology (2)
- Time series analysis (2)
- Visual Analytics (2)
- ADAMS modeling (1)
- Analytics (1)
-
- Constitutional right AND drugs (1)
- Consumer behavior (1)
- Data analysis (1)
- Data communication (1)
- Data interpretation (1)
- Decision-making (1)
- Deep neural networks (1)
- Drug decriminalization (1)
- Drug legalization (1)
- Drug overdose (1)
- Drug trafficking (1)
- Drugs (1)
- Dynamic Covariance Kernel Density Estimation (1)
- Economics AND drugs (1)
- Financial modelling (1)
- Fintech (1)
- Forecasting (1)
- GDP growth (1)
- GPU utilization (1)
- Health effects AND drugs (1)
- Information visualization (1)
- Linear regression (1)
- Lucky Cement (1)
- Machine learning (1)
- Method of moving averages (1)
- Publication
-
- Annual Symposium on Biomathematics and Ecology Education and Research (2)
- Biology and Medicine Through Mathematics Conference (2)
- Modeling, Simulation and Visualization Student Capstone Conference (2)
- The Summer Undergraduate Research Fellowship (SURF) Symposium (2)
- Undergraduate Student Research Internships Conference (2)
-
- Annual Research Symposium (1)
- CBER Conference (1)
- Helm's School of Government Conference - American Revival: Citizenship & Virtue (1)
- International Conference on Gambling & Risk Taking (1)
- National Youth Advocacy and Resilience Conference (1)
- SDSU Data Science Symposium (1)
- The 8th International Conference on Physical and Numerical Simulation of Materials Processing (1)
- UVM Libraries Conference Day (1)
- File Type
Articles 1 - 18 of 18
Full-Text Articles in Longitudinal Data Analysis and Time Series
Lstm-Based Recurrent Neural Network Predicts Influenza-Like-Illness In Variable Climate Zones, Alfred Amendolara, Christopher Gowans, Joshua Barton, David Sant, Andrew Payne
Lstm-Based Recurrent Neural Network Predicts Influenza-Like-Illness In Variable Climate Zones, Alfred Amendolara, Christopher Gowans, Joshua Barton, David Sant, Andrew Payne
Annual Research Symposium
Purpose: Influenza virus is responsible for a recurrent, yearly epidemic in most temperate regions of the world. For the 2021-2022 season the CDC reports 5000 deaths and 100,000 hospitalizations, a significant number despite the confounding presence of SARS-CoV-2. The mechanisms behind seasonal variance in flu burden are not well understood. Based on a previously validated model, this study seeks to expand understanding of the impact of variable climate regions on seasonal flu trends. To that end, three climate regions have been selected. Each region represents a different ecological region and provides different weather patterns showing how the climate variables impact …
The Double Edged Sword Of The Pandemic: Exploring Associations Between Covid-19 And Social Isolation In The Usa, Alexander Fulk
The Double Edged Sword Of The Pandemic: Exploring Associations Between Covid-19 And Social Isolation In The Usa, Alexander Fulk
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Payments Data In Gambling Research, Kasra Ghaharian, Mana Azizsoltani
Payments Data In Gambling Research, Kasra Ghaharian, Mana Azizsoltani
International Conference on Gambling & Risk Taking
A considerable body of gambling-related research has leveraged gamblers' behavioral tracking data to address a broad set of research questions. These data have typically comprised of gamblers' betting-related behaviors including, for example, the frequency and volume of betting. The analysis of gamblers' payment-related behavioral data is far less common, but provides a fruitful avenue gambling-related research.
In this presentation we discuss a selection of potential research opportunities that payments transaction data presents. We supplement this discussion with specific analyses that have been performed by our research group. We also discuss knowledge gaps and areas for future research.
A Novel Family Of Chain Binomial Models To Investigate Correlated Vaccination And Infection Rates In Sveirs Epidemic Dynamics, Divine Wanduku
A Novel Family Of Chain Binomial Models To Investigate Correlated Vaccination And Infection Rates In Sveirs Epidemic Dynamics, Divine Wanduku
Biology and Medicine Through Mathematics Conference
No abstract provided.
Drug Ideologies Of The United States, Macy Montgomery
Drug Ideologies Of The United States, Macy Montgomery
Helm's School of Government Conference - American Revival: Citizenship & Virtue
The United States has been increasingly creating lenient drug policies. Seventeen states and Washington, the District of Columbia, legalized marijuana, and Oregon decriminalized certain drugs, including methamphetamine, heroin, and cocaine. The medical community has proven that drugs, including marijuana, have myriad adverse health side effects. This leads to two questions: Why does the United States government continue to create lenient drug policies, and what reasons do citizens give for legalizing drugs when the medical community has proven them harmful? The paper hypothesizes that the disadvantages of drug legalization outweigh its benefits because of the numerous harms it causes, such as …
Gpu Utilization: Predictive Sarimax Time Series Analysis, Dorothy Dorie Parry
Gpu Utilization: Predictive Sarimax Time Series Analysis, Dorothy Dorie Parry
Modeling, Simulation and Visualization Student Capstone Conference
This work explores collecting performance metrics and leveraging the output for prediction on a memory-intensive parallel image classification algorithm - Inception v3 (or "Inception3"). Experimental results were collected by nvidia-smi on a computational node DGX-1, equipped with eight Tesla V100 Graphic Processing Units (GPUs). Time series analysis was performed on the GPU utilization data taken, for multiple runs, of Inception3’s image classification algorithm (see Figure 1). The time series model applied was Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX).
The Effectiveness Of Visualization Techniques For Supporting Decision-Making, Cansu Yalim, Holly A. H. Handley
The Effectiveness Of Visualization Techniques For Supporting Decision-Making, Cansu Yalim, Holly A. H. Handley
Modeling, Simulation and Visualization Student Capstone Conference
Although visualization is beneficial for evaluating and communicating data, the efficiency of various visualization approaches for different data types is not always evident. This research aims to address this issue by investigating the usefulness of several visualization techniques for various data kinds, including continuous, categorical, and time-series data. The qualitative appraisal of each technique's strengths, weaknesses, and interpretation of the dataset is investigated. The research questions include: which visualization approaches perform best for different data types, and what factors impact their usefulness? The absence of clear directions for both researchers and practitioners on how to identify the most effective visualization …
Models For Predicting Maximum Potential Intensity Of Tropical Cyclones, Iftekhar Chowdhury, Gemechis Djira
Models For Predicting Maximum Potential Intensity Of Tropical Cyclones, Iftekhar Chowdhury, Gemechis Djira
SDSU Data Science Symposium
Tropical cyclones (TCs) are considered as extreme weather events, which has a low-pressure center, namely an eye, strong winds, and a spiral arrangement of thunderstorms that produces heavy rain, storm surges, and can cause severe destruction in coastal areas worldwide. Therefore, reliable forecasts of the maximum potential intensity (MPI) of TCs are critical to estimate the damages to properties, lives, and risk assessment. In this study, we explore and propose various regression models, to predict the potential intensity of TCs in the North Atlantic at 12, 24, 36, 48, 60, and 72- hour forecasting lead time. In addition, a popular …
A Transformer-Based Classification System For Volcanic Seismic Signals, Anthony P. Rinaldi, Cindy Mora Stock, Cristián Bravo Roman, Alexander Hemming
A Transformer-Based Classification System For Volcanic Seismic Signals, Anthony P. Rinaldi, Cindy Mora Stock, Cristián Bravo Roman, Alexander Hemming
Undergraduate Student Research Internships Conference
Monitoring volcanic events as they occur is a task that, to this day, requires significant human capital. The current process requires geologists to monitor seismographs around the clock, making it extremely labour-intensive and inefficient. The ability to automatically classify volcanic events as they happen in real-time would allow for quicker responses to these events by the surrounding communities. Timely knowledge of the type of event that is occurring can allow these surrounding communities to prepare or evacuate sooner depending on the magnitude of the event. Up until recently, not much research has been conducted regarding the potential for machine learning …
Functional Structure Of Excess Return And Volatility, Chenxi Zhao
Functional Structure Of Excess Return And Volatility, Chenxi Zhao
Undergraduate Student Research Internships Conference
Capturing the relation between excess returns and volatility can help making better decisions in the stock market in terms of portfolio allocation and assets risk management. This paper takes the data of a minute-by-minute series of S&P500 from January 2009 to January 2021 as the research object and explores the best structural representation for the excess return as a function of the volatility, for a well-known index. This is implemented via regression models for volatility and excess returns. The results reveal that there’s a structural break in the relationship between the excess return and volatility based on the sign of …
Intervention Time Series Analysis Of Organ Donor Transplants In The Us, Supraja Malladi
Intervention Time Series Analysis Of Organ Donor Transplants In The Us, Supraja Malladi
Biology and Medicine Through Mathematics Conference
No abstract provided.
Demand Forecasting For Lucky Cement, Muhammad Arsalan Rashid
Demand Forecasting For Lucky Cement, Muhammad Arsalan Rashid
CBER Conference
As we know that demand is the Quantities of a good or service that people are ready to buy at various prices within some given time, other factors besides price held constant I tried to forecast the sales for next years. I removed seasonality factors and applied other determinants to predict the demand. By using values of independent variables in my Regression, the Annual Sales of Lucky Cement for period 2020-2021 is found to be around 7.9 Million Tons.
Snakebite Dynamics Of Colombia: Effects Of Precipitation Seasonality Of Incidence, Carlos Cruz
Snakebite Dynamics Of Colombia: Effects Of Precipitation Seasonality Of Incidence, Carlos Cruz
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Building A Better Risk Prevention Model, Steven Hornyak
Building A Better Risk Prevention Model, Steven Hornyak
National Youth Advocacy and Resilience Conference
This presentation chronicles the work of Houston County Schools in developing a risk prevention model built on more than ten years of longitudinal student data. In its second year of implementation, Houston At-Risk Profiles (HARP), has proven effective in identifying those students most in need of support and linking them to interventions and supports that lead to improved outcomes and significantly reduces the risk of failure.
Nondestructive Testing And Structural Health Monitoring Based On Adams And Svm Techniques, Gang Jiang, Yi Ming Deng, Ji Tai Niu
Nondestructive Testing And Structural Health Monitoring Based On Adams And Svm Techniques, Gang Jiang, Yi Ming Deng, Ji Tai Niu
The 8th International Conference on Physical and Numerical Simulation of Materials Processing
No abstract provided.
Passive Visual Analytics Of Social Media Data For Detection Of Unusual Events, Kush Rustagi, Junghoon Chae
Passive Visual Analytics Of Social Media Data For Detection Of Unusual Events, Kush Rustagi, Junghoon Chae
The Summer Undergraduate Research Fellowship (SURF) Symposium
Now that social media sites have gained substantial traction, huge amounts of un-analyzed valuable data are being generated. Posts containing images and text have spatiotemporal data attached as well, having immense value for increasing situational awareness of local events, providing insights for investigations and understanding the extent of incidents, their severity, and consequences, as well as their time-evolving nature. However, the large volume of unstructured social media data hinders exploration and examination. To analyze such social media data, the S.M.A.R.T system provides the analyst with an interactive visual spatiotemporal analysis and spatial decision support environment that assists in evacuation planning …
Spatiotemporal Crime Analysis, James Q. Tay, Abish Malik, Sherry Towers, David Ebert
Spatiotemporal Crime Analysis, James Q. Tay, Abish Malik, Sherry Towers, David Ebert
The Summer Undergraduate Research Fellowship (SURF) Symposium
There has been a rise in the use of visual analytic techniques to create interactive predictive environments in a range of different applications. These tools help the user sift through massive amounts of data, presenting most useful results in a visual context and enabling the person to rapidly form proactive strategies. In this paper, we present one such visual analytic environment that uses historical crime data to predict future occurrences of crimes, both geographically and temporally. Due to the complexity of this analysis, it is necessary to find an appropriate statistical method for correlative analysis of spatiotemporal data, as well …
Bailey/Howe Reference Analytics: What Two Years Of Data Tell Us, Elizabeth Berman
Bailey/Howe Reference Analytics: What Two Years Of Data Tell Us, Elizabeth Berman
UVM Libraries Conference Day
Analyzing the last two academic years (2010-2011 and 2011-2012) of reference-desk statistics, this presentation will highlight trends at the Bailey/Howe Reference Desk, and offer scenarios for the future of reference services.