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Graduate Theses & Dissertations
Pages
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- Application of One-factor Models for Prices of Crops and Option Pricing Process
- This thesis is intended to support dependent-on-crops farmers to hedge the price risks of their crops. Firstly, we applied one-factor model, which incorporated a deterministic function and a stochastic process, to predict the future prices of crops (soybean). A discrete form was employed for one-month-ahead prediction. For general prediction, de-trending and de-cyclicality were used to remove the deterministic function. Three candidate stochastic differential equations (SDEs) were chosen to simulate the stochastic process; they are mean-reverting Ornstein-Uhlenbeck (OU) process, OU process with zero mean, and Brownian motion with a drift. Least squares methods and maximum likelihood were used to estimate the parameters. Results indicated that one-factor model worked well for soybean prices. Meanwhile, we provided a two-factor model as an alternative model and it also performed well in this case. In the second main part, a zero-cost option package was introduced and we theoretically analyzed the process of hedging. In the last part, option premiums obtained based on one-factor model could be compared to those obtained from Black-Scholes model, thus we could see the differences and similarities which suggested that the deterministic function especially the cyclicality played an essential role for the soybean price, thus the one-factor model in this case was more suitable than Black-Scholes model for the underlying asset. Author Keywords: Brownian motion, Least Squares Method, Maximum Likelihood Method, One-factor Model, Option Pricing, Ornstein-Uhlenbeck Process
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- Utilizing Class-Specific Thresholds Discovered by Outlier Detection
- We investigated if the performance of selected supervised machine-learning techniques could be improved by combining univariate outlier-detection techniques and machine-learning methods. We developed a framework to discover class-specific thresholds in class probability estimates using univariate outlier detection and proposed two novel techniques to utilize these class-specific thresholds. These proposed techniques were applied to various data sets and the results were evaluated. Our experimental results suggest that some of our techniques may improve recall in the base learner. Additional results suggest that one technique may produce higher accuracy and precision than AdaBoost.M1, while another may produce higher recall. Finally, our results suggest that we can achieve higher accuracy, precision, or recall when AdaBoost.M1 fails to produce higher metric values than the base learner. Author Keywords: AdaBoost, Boosting, Classification, Class-Specific Thresholds, Machine Learning, Outliers
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- Modeling drought derivatives in arid regions
- We propose a stochastic weather model based on temperature, precipitation, humidity and wind speed for Qatar, as a representative arid region, in order to obtain simulated values for a drought index. As a drought index, the Reconnaissance Drought Index (RDI) is commonly accepted in agriculture and is used to measure drought severity. It can be used to price weather derivatives to help farmers reduce nancial losses from drought. RDI, which is the ratio of precipitation to evapotranspiration, is calculated by considering crop growth stages. The use of dierent crop coecient value depending on the growth stage to calculate evapotranspiration can provide improved values for RDI. Additionally, six calculation methods for evapotranspiration using weather data are investigated to obtain accurate values for RDI. Author Keywords: Evapotranspiration, Markov chains, Mean reversion processes, Reconnaissance Drought Index, Stochastic dierential equations, Stochastic weather models
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- Educational Data Mining and Modelling on Trent University Students’ Academic Performance
- Higher education is important. It enhances both individual and social welfare by improving productivity, life satisfaction, and health outcomes, and by reducing rates of crime. Universities play a critical role in providing that education. Because academic institutions face resource constraints, it is thus important that they deploy resources in support of student success in the most efficient ways possible. To inform that efficient deployment, this research analyzes institutional data reflecting undergraduate student performance to identify predictors of student success measured by GPA, rates of credit accumulation, and graduation rates. Using methods of cluster analysis and machine learning, the analysis yields predictions for the probabilities of individual success. Author Keywords: Educational data mining, Students’ academic performance modelling
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- Modelling Request Access Patterns for Information on the World Wide Web
- In this thesis, we present a framework to model user object-level request patterns in the World Wide Web.This framework consists of three sub-models: one for file access, one for Web pages, and one for storage sites. Web Pages are modelled to be made up of different types and sizes of objects, which are characterized by way of categories. We developed a discrete event simulation to investigate the performance of systems that utilize our model.Using this simulation, we established parameters that produce a wide range of conditions that serve as a basis for generating a variety of user request patterns. We demonstrated that with our framework, we can affect the mean response time (our performance metric of choice) by varying the composition of Web pages using our categories. To further test our framework, it was applied to a Web caching system, for which our results showed improved mean response time and server load. Author Keywords: discrete event simulation (DES), Internet, performance modelling, Web caching, World Wide Web
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- Prescription Drugs
- Medication used to treat human illness is one of the greatest developments in human history. In Canada, prescription drugs have been developed and made available to treat a wide variety of illnesses, from infections to heart disease and so on. Records of prescription drug fulfillment at coarse Canadian geographic scales were obtained from Health Canada in order to track the use of these drugs by the Canadian population. The obtained prescription drug fulfillment records were in a variety of inconsistent formats, including a large selection of years for which only paper tabular records were available (hard copies). In this work, we organize, digitize, proof and synthesize the full available data set of prescription drug records, from paper to final database. Extensive quality control was performed on the data before use. This data was then analyzed for temporal and spatial changes in prescription drug use across Canada from 1990-2013. In addition, one of major research areas in environmental epidemiological studies is the study of population health risk associated with exposure to ambient air pollution. Prescription drugs can moderate public health risk, by reducing the drug user's physiological symptoms and preventing acute health effects (e.g., strokes, heart attacks, etc.). The cleaned prescription drug data was considered in the context of a common model to examine its influence on the association between air pollution exposure and various health outcomes. Since, prescription drug data were available only at the provincial level, a Bayesian hierarchical model was employed to include the prescription drugs as a covariate at regional level, which were then combined to estimate the association at national level. Although further investigations are required, the study results suggest that the prescription drugs influenced the air pollution related public health risk. Author Keywords: Data, Error checking, Population health, Prescriptions
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- Modelling Depressive Symptoms in Emerging Adulthood
- Depression during the transition into adulthood is a growing mental health concern, with overwhelming evidence linking the developmental risk for depressive symptoms with maternal depression. In addition, there is a lack of research on the protective role of socioemotional competencies in this context. This study examines independent and joint effects of maternal depression and trait emotional intelligence (TEI) on the longitudinal trajectory of depressive symptoms during emerging adulthood. A series of latent growth models was applied to three biennial cycles of data from a nationally representative sample (N=933) from the Canadian National Longitudinal Survey of Children and Youth. We assessed the trajectory of self-reported depressive symptoms from age 20 to 24 years, as well as whether it was moderated by maternal depression at age 10 to 11 and TEI at age 20, separately by gender. The results indicated that mean levels of depression declined during the emerging adulthood in females, but remained relatively stable in males. Maternal depressive symptoms significantly positively predicted depressive symptoms across the entire emerging adulthood in females, but only at age 20-21 for males. In addition, likelihood of developing depressive symptoms was attenuated by higher global TEI in both females and males, and additionally by higher interpersonal skills in males. Our findings suggest that interventions for depressive symptoms in emerging adulthood should consider development of socioemotional competencies. Author Keywords: Depression, Depressive Symptoms, Emerging Adulthood, Intergenerational Risk, Longitudinal, Trait Emotional Intelligence
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- Exploring the Scalability of Deep Learning on GPU Clusters
- In recent years, we have observed an unprecedented rise in popularity of AI-powered systems. They have become ubiquitous in modern life, being used by countless people every day. Many of these AI systems are powered, entirely or partially, by deep learning models. From language translation to image recognition, deep learning models are being used to build systems with unprecedented accuracy. The primary downside, is the significant time required to train the models. Fortunately, the time needed for training the models is reduced through the use of GPUs rather than CPUs. However, with model complexity ever increasing, training times even with GPUs are on the rise. One possible solution to ever-increasing training times is to use parallelization to enable the distributed training of models on GPU clusters. This thesis investigates how to utilise clusters of GPU-accelerated nodes to achieve the best scalability possible, thus minimising model training times. Author Keywords: Compute Canada, Deep Learning, Distributed Computing, Horovod, Parallel Computing, TensorFlow
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- Fraud Detection in Financial Businesses Using Data Mining Approaches
- The purpose of this research is to apply four methods on two data sets, a Synthetic dataset and a Real-World dataset, and compare the results to each other with the intention of arriving at methods to prevent fraud. Methods used include Logistic Regression, Isolation Forest, Ensemble Method and Generative Adversarial Networks. Results show that all four models achieve accuracies between 91% and 99% except Isolation Forest gave 69% accuracy for the Synthetic dataset. The four models detect fraud well when built on a training set and tested with a test set. Logistic Regression achieves good results with less computational eorts. Isolation Forest achieve lower results accuracies when the data is sparse and not preprocessed correctly. Ensemble Models achieve the highest accuracy for both datasets. GAN achieves good results but overts if a big number of epochs was used. Future work could incorporate other classiers. Author Keywords: Ensemble Method, GAN, Isolation forest, Logistic Regression, Outliers
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- Relationship Between Precarious Employment, Behaviour Addictions and Substance Use Among Canadian Young Adults
- This thesis utilized a unique data-set, the Quinte Longitudinal Survey, to explore relationships among precarious employment and a range of mental health problems in a representative sample of Ontario young adults. Study 1 focused on various behavioural addictions (such as problem gambling, video gaming, internet use, exercise, compulsive shopping, and sex) and precarious employment. The results showed that precariously employed men were preoccupied with gambling and sex while their female counterparts preferred shopping. Gambling and excessive shopping diminished over time while excessive sexual practices increased. Study 2 focused on the association between precarious employment and substance abuse (such as tobacco, alcohol, cannabis, hallucinogens, stimulants, and other substances). The results showed that men used cannabis more than women, and the non-precarious employed group abused alcohol more than individuals in the precarious group. This research has implications for both health care professionals and intervention program developers when working with young adults in precarious jobs. Author Keywords: Behaviour Addictions, Precarious Employment, Substance Abuse, Young Adults
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- Predicting Irregularities in Arrival Times for Toronto Transit Buses with LSTM Recurrent Neural Networks Using Vehicle Locations and Weather Data
- Public transportation systems play important role in the quality of life of citizens in any metropolitan city. However, public transportation authorities face criticisms from commuters due to irregularities in bus arrival times. For example, transit bus users often complain when they miss the bus because it arrived too early or too late at the bus stop. Due to these irregularities, commuters may miss important appointments, wait for too long at the bus stop, or arrive late for work. This thesis seeks to predict the occurrence of irregularities in bus arrival times by developing machine learning models that use GPS locations of transit buses provided by the Toronto Transit Commission (TTC) and hourly weather data. We found that in nearly 37% of the time, buses either arrive early or late by more than 5 minutes, suggesting room for improvement in the current strategies employed by transit authorities. We compared the performance of three machine learning models, for which our Long Short-Term Memory (LSTM) [13] model outperformed all other models in terms of accuracy. The error rate for LSTM model was the lowest among Artificial Neural Network (ANN) and support vector regression (SVR). The improved accuracy achieved by LSTM is due to its ability to adjust and update the weights of neurons while maintaining long-term dependencies when encountering new stream of data. Author Keywords: ANN, LSTM, Machine Learning
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- Psychometric Properties of a Scale Developed from a Three-Factor Model of Social Competency
- While existing models of emotional intelligence (EI) generally recognize the importance of social competencies (SC), there is a tendency in the literature to narrow the focus to competencies that pertain to the self. Given the experiential and perceptual differences between self- vs. other-oriented emotional abilities, this is an important limitation of existing EI models and assessment tools. This thesis explores the psychometric properties of a multidimensional model for SC. Chapter 1 describes the evolution of work on SCs in modern psychology and describes the multidimensional model of SC under review. Chapter 2 replicates this model across a variety of samples and explores the model’s construct validity via basic personality and EI constructs. Chapter 3 further explores the predictive validity of the SC measure within a group of project managers and several success and wellness variables. Chapter 4 examines potential applications for the model and suggestions for further research. Author Keywords: emotional intelligence, project management, social competency, work readiness