Longitudinal Data Analysis and Time Series Commons^™

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).

Extending The M3-Competition: Category And Interval-Specific Time Series Forecasting, Will Sherman, Kati Schuerger, Randy Kim, Bivin Sadler

SMU Data Science Review

The M3-Competition found that simple models outperform more complex ones for time series forecasting. As part of these competitions, several claims were made that statistical models exceeded machine learning (ML) techniques, such as recurrent neural networks (RNN), in prediction performance. These findings may over-generalize the capabilities of statistical models since the analysis measured the total forecasting accuracy across a wide range of industries and fields and with different interval lengths. This investigation aimed to assess how statistical and ML methods compared when individuating series by category and time interval. Utilizing the M3 data and building individual models using Facebook© Prophet …

Stock Forecasts With Lstm And Web Sentiment, Michael Burgess, Faizan Javed, Nnenna Okpara, Chance Robinson

SMU Data Science Review

Traditional time-series techniques, such as auto-regressive and moving average models, can have difficulties when applied to stock data due to the randomness inherent to the markets. In this study, Long Short-Term Memory Recurrent Neural Networks, or LSTMs, have been applied to pricing data along with sentiment scores derived from web sources such as Twitter and other financial media outlets. The project team utilized this approach to complement the technical indicators observed at the end of each trading day for three stocks from the NASDAQ stock exchange over a 12-year span. A common benchmark to assess model performance on time series …

Realtime Event Detection In Sports Sensor Data With Machine Learning, Mallory Cashman

Honors Theses and Capstones

Machine learning models can be trained to classify time series based sports motion data, without reliance on assumptions about the capabilities of the users or sensors. This can be applied to predict the count of occurrences of an event in a time period. The experiment for this research uses lacrosse data, collected in partnership with SPAITR - a UNH undergraduate startup developing motion tracking devices for lacrosse. Decision Tree and Support Vector Machine (SVM) models are trained and perform with high success rates. These models improve upon previous work in human motion event detection and can be used a reference …

Estimating The Statistics Of Operational Loss Through The Analyzation Of A Time Series, Maurice L. Brown

Theses and Dissertations

In the world of finance, appropriately understanding risk is key to success or failure because it is a fundamental driver for institutional behavior. Here we focus on risk as it relates to the operations of financial institutions, namely operational risk. Quantifying operational risk begins with data in the form of a time series of realized losses, which can occur for a number of reasons, can vary over different time intervals, and can pose a challenge that is exacerbated by having to account for both frequency and severity of losses. We introduce a stochastic point process model for the frequency distribution …

Institutional Context Drives Mobility: A Comprehensive Analysis Of Academic And Economic Factors That Influence International Student Enrollment At United States Higher Education Institutions, Natalie Cruz

College of Education & Professional Studies (Darden) Posters

International student enrollment (ISE) has become a hallmark of world-class higher education institutions (HEIs). Although the U.S. has welcomed the largest numbers of international students since the 1950s, ISE shrunk by 10% in the previous three years from an all-time high of 903,127 students in 2016/2017 (IIE, 2019). Research studies about international student mobility and enrollment highlights the significant role that academic and economic rationales play for international students. This quantitative, ex post facto study focused on the influence of ranking, tuition, Optional Practical Training, Gross Domestic Product, and the unemployment rate on ISE at 2,884 U.S. HEIs from 2008 …

Time Series Analysis Of Offshore Buoy Light Detection And Ranging (Lidar) Windspeed Data, Aditya Garapati, Charles J. Henderson, Carl Walenciak, Brian T. Waite

SMU Data Science Review

In this paper, modeling techniques for the forecasting of wind speed using historical values observed by Light Detection and Ranging (LIDAR) sensors in an offshore context are described. Both univariate time series and multivariate time series modeling techniques leveraging meteorological data collected simultaneously with the LIDAR data are evaluated for potential contributions to predictive ability. Accurate and timely ability to predict wind values is essential to the effective integration of wind power into existing power grid systems. It allows for both the management of rapid ramp-up / down of base production capacity due to highly variable wind power inputs and …

Phenotype Extraction: Estimation And Biometrical Genetic Analysis Of Individual Dynamics, Kevin L. Mckee

Theses and Dissertations

Within-person data can exhibit a virtually limitless variety of statistical patterns, but it can be difficult to distinguish meaningful features from statistical artifacts. Studies of complex traits have previously used genetic signals like twin-based heritability to distinguish between the two. This dissertation is a collection of studies applying state-space modeling to conceptualize and estimate novel phenotypic constructs for use in psychiatric research and further biometrical genetic analysis. The aims are to: (1) relate control theoretic concepts to health-related phenotypes; (2) design statistical models that formally define those phenotypes; (3) estimate individual phenotypic values from time series data; (4) consider hierarchical …

Feasibility Of Multi-Year Forecast For The Colorado River Water Supply: Time Series Modeling, Brian Plucinski

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

The Colorado River is one of the largest resources for water in the United States, as well as being an important asset to the economy. Previous studies have shown a connection between the Great Salt Lake and the Colorado River. This study used time series analysis to build models to predict the water supply of the Colorado River ten years out. These models used data from the Colorado River in addition to Great Salt Lake water elevation. Several models suggest a decline in water supply from 2013 – 2020, before starting to increase. These predictions differ from predictions published by …

Analysis Of Break-Points In Financial Time Series, Jean Remy Habimana

Graduate Theses and Dissertations

A time series is a set of random values collected at equal time intervals; this randomness makes these types of series not easy to predict because the structure of the series may change at any time. As discussed in previous research, the structure of time series may change at any time due to the change in mean and/or variance of the series. Consequently, based on this structure, it is wise not to assume that these series are stationary. This paper, discusses, a method of analyzing time series by considering the entire series non-stationary, assuming there is random change in unconditional …

Key Factors Driving Personnel Downsizing In Multinational Military Organizations, Ilksen Gorkem, Resit Unal, Pilar Pazos

Engineering Management & Systems Engineering Faculty Publications

Although downsizing has long been a topic of research in traditional organizations, there are very few studies of this phenomenon in military contexts. As a result, we have little understanding of the key factors that drive personnel downsizing in military setting. This study contributes to our understanding of key factors that drive personnel downsizing in military organizations and whether those factors may differ across NATO nations’ cultural clusters. The theoretical framework for this study was built from studies in non-military contexts and adapted to fit the military environment.

This research relies on historical data from one of the largest multinational …

Geographic And Temporal Epidemiology Of Campylobacteriosis, Jennifer Weisent

Doctoral Dissertations

Campylobacteriosis is a leading cause of gastroenteritis in the United States. The focus of this research was to (i) analyze and predict spatial and temporal patterns and associations for campylobacteriosis risk and (ii) compare the utility of advanced modeling methods. Laboratory-confirmed Campylobacter case data, obtained from the Foodborne Diseases Active Surveillance Network were used in all investigations.

We compared the accuracy of forecasting techniques for campylobacteriosis risk in Minnesota, Oregon and Georgia and found that time series regression, decomposition, and Box-Jenkins Autoregressive Integrated Moving Averages reliably predict monthly risk of infection for campylobacteriosis. Decomposition provided the fastest, most accurate, user-friendly …

Interactions Between Serotypes Of Dengue Highlight Epidemiological Impact Of Cross-Immunity, Nicholas Reich, Sourya Shrestha, Aaron King, Pejman Rohani, Justin Lessler, Siripen Kalayanarooj, In-Kyu Yoon, Robert Gibbons, Donald Burke, Derek Cummings

Nicholas G Reich

Dengue, a mosquito-borne virus of humans, infects over 50 million people annually. Infection with any of the four dengue serotypes induces protective immunity to that serotype, but does not confer long-term protection against infection by other serotypes. The immunological interactions between sero- types are of central importance in understanding epidemiological dynamics and anticipating the impact of dengue vaccines. We analysed a 38-year time series with 12 197 serotyped dengue infections from a hospital in Bangkok, Thailand. Using novel mechanistic models to represent different hypothesized immune interactions between serotypes, we found strong evidence that infec- tion with dengue provides substantial short-term …

Methods For The Analysis Of Developmental Respiration Patterns., Justin Tyler Peyton

Electronic Theses and Dissertations

This thesis looks at the problem of developmental respiration in Sarcophaga crassipalpis Macquart from the biological and instrumental points of view and adapts mathematical and statistical tools in order to analyze the data gathered. The biological motivation and current state of research is given as well as instrumental considerations and problems in the measurement of carbon dioxide production. A wide set of mathematical and statistical tools are used to analyze the time series produced in the laboratory. The objective is to assemble a methodology for the production and analysis of data that can be used in further developmental respiration research.

Significance Analysis Of Time Course Microarray Experiments, John D. Storey, Wenzhong Xiao, Jeffrey T. Leek, Ronald G. Tompkins, Ron W. Davis

UW Biostatistics Working Paper Series

Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to in vivo endotoxin administration. Using our method 7409 genes are called significant at a 1% …