This PhD dissertation comprises four essays on forecasting financial markets with unsupervised predictive analytics techniques, most notably time series extrapolation methods and artificial neural networks. Key objectives of the research were reproducibility and replicability, which are fundamental principles in management science and, as such, the implementation of all of the suggested algorithms has been fully automated and completely unsupervised in R. As with any predictive analytics exercise, computational intensiveness is a significant challenge and criterion of performance and, thus, both forecasting accuracy and uncertainty as well as computational times are reported in all essays. Multiple horizons, multiple methods and benchmarks and multiple metrics are employed as dictated by good practice in empirical forecasting exercises. The essays evolve in nature as each one is based on the previous one, testing one more condition as the essays progress, outlined in sequence as follows: which method wins overall in a very extensive evaluation over five frequencies (yearly, quarterly, monthly, weekly and daily data) over 18 time series of stocks with the biggest capitalization from the FTSE 100, over the last 20 years (first essay); the impact of horizon in this exercise and how this promotes different winners for different horizons (second essay); the impact of using uncertainty in the form of maximum-minimum values per period, despite still being interested in forecasting the mean expected value over the next period; and introducing a second variable capturing all other aspects of the behavioural nature of the financial environment – the trading volume – and evaluating whether this improves forecasting performance or not. The whole endeavour required the use of the High Performance Computing Wales (HPC Wales) for a significant amount of time, incurring computational costs that ultimately paid off in terms of increased forecasting accuracy for the AI approaches; the whole exercise for one series can be repeated on a fast laptop device (i7 with 16 GB of memory). Overall (forecasting) horses for (data) courses were once again proved to perform best, and the fact that one method cannot win under all conditions was once more evidenced. The introduction of uncertainty (in terms of range for every period), as well as volume as a second variable capturing environmental aspects, was beneficial with regard to forecasting accuracy and, overall, the research provided empirical evidence that predictive analytics approaches have a future in such a forecasting context. Given this was a predictive analytics exercise, focus was placed on forecasting levels (monetary values) and not log-returns; and out-of-sample forecasting accuracy, rather than causality, was a primary objective, thus multiple regression models were not considered as benchmarks. As in any empirical predicting analytics exercise, more time series, more artificial intelligence methods, more metrics and more data can be employed so as to allow for full generalization of the results, as long as all of these can be fully automated and forecast unsupervised in a freeware environment – in this thesis that being R.

As computing matures, it is becoming increasingly obvious that a change is necessary for the manner in which web services interact with users. Server-centric models are inconvenient for users. A new paradigm, Smart Interactions, provides a web service architecture which is centered around the user's needs, rather than the simplistic server view currently being used. The system responds to the individual user and is able to adapt to changes to better serve the user. The Smart Internet system helps the user accomplish their tasks efficiently and intuitively. An important aspect of Smart Interactions is that of cognitive support, which provides enhanced information and guidance to the system or user linked to the current task. This thesis examines predictive analytics and its application to cognitive support in Smart Interactions, and presents and evaluates a framework for using predictive analytic support within the Smart Internet model. / Thesis (Master, Computing) -- Queen's University, 2011-09-29 18:11:02.374

Despite the emergence of artificial intelligence (AI) and machine learning (ML) in medicine and the resultant interest in predictive analytics in surgery, there remains a paucity of research on the actual impact of prediction models and their effect on surgeons’ risk assessment of post-surgical complications. This research evaluated how spinal surgeons predict post-surgical complications with and without additional information generated by a ML predictive model. The study was conducted in two stages. In the preliminary stage an ML prediction model for post-surgical complications in spine surgery was developed. In the second stage, a survey instrument was developed, using patient vignettes, to determine how providing ML model support affected surgeons’ predictions of post-surgical complications. Results show that support provided by a ML prediction model improved surgeons’ accuracy to correctly predict the presence or absence of a complication in patients undergoing spinal surgery from 49.1% to 54.8% (p=0.024). It is clear that predicting post-surgical complications in patients undergoing spinal surgery is difficult, for models and experienced surgeons, but it is not surprising that additional information provided by the ML model prediction was beneficial overall. This is the first study in the spine surgery literature that has evaluated the impact of a ML prediction model on surgeon prediction accuracy of post-surgical complications.

In recent years, the American Association of Colleges of Pharmacy (AACP) has encouraged the application of big data analytic techniques to pharmaceutical education. Indeed, the 2013-2014 Academic Affairs Committee Report included a "Learning Analytics in Pharmacy Education" section that reviewed the potential benefits of adopting big data techniques.1 Likewise, the 2014-2015 Argus Commission Report discussed uses for big data analytics in the classroom, practice, and admissions.2 While both of these reports were thorough, neither discussed specific analytic techniques. Consequently, this commentary will introduce classification trees, with a particular emphasis on their use in admission. With electronic applications, pharmacy schools and colleges now have access to detailed applicant records containing thousands of observations. With declining applications nationwide, admissions analytics may be more important than ever.3.

This dissertation looks at how new sources of information should be incorporated into medical decision-making processes to improve patient outcomes and reduce costs. There are three fundamental challenges that must be overcome to effectively use personalized medicine, we need to understand: 1) how best to appropriately designate which patients will receive the greatest value from these processes; 2) how physicians and caregivers interpret additional patient-specific information and how that affects their decision-making processes; and finally, (3) how to account for a patient’s ability to engage in their own healthcare decisions. The first study looks at how we can infer which patients will receive the most value from genomic testing. The difficult statistical problem is how to separate the distribution of patients, based on ex-ante factors, to identify the best candidates for personalized testing. A model was constructed to infer a healthcare provider’s decision on whether this test would provide beneficial information in selecting a patient’s medication. Model analysis shows that healthcare providers’ primary focus is to maximize patient health outcomes while considering the impact the patient’s economic welfare. The second study focuses on understanding how technology-enabled continuity of care (TECC) for Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF) patients can be utilized to improve patient engagement, measured in terms of patient activation. We shed light on the fact that different types of patients garnered different levels of value from the use of TECC. The third study looks at how data-driven decision support systems can allow physicians to more accurately understand which patients are at high-risk of readmission. We look at how we can use available patient-specific information for patients admitted with CHF to more accurately identify which patients are most likely to be readmitted, and also why – whether for condition-related reasons versus for non- related reasons, allowing physicians to suggest different patient-specific readmission prevention strategies. Taken together, these three studies allow us to build a robust theory to tackle these challenges, both operational and policy-related, that need to be addressed for physicians to take advantage of the growing availability of patient-specific information to improve personalized medication processes.

Investigations of tokamak dynamics, especially as they relate to the challenge of burn control, require an accurate representation of energy and particle confinement times. While the ITER-98 scaling law represents a correlation of data from a wide range of tokamaks, confinement scaling laws will need to be fine-tuned to specific operational features of specific tokamaks in the future. A methodology for developing, by regression analysis, tokamak- and configuration-specific confinement tuning models is presented and applied to DIII-D as an illustration. It is shown that inclusion of tuning parameters in the confinement models can significantly enhance the agreement between simulated and experimental temperatures relative to simulations in which only the ITER-98 scaling law is used. These confinement tuning parameters can also be used to represent the effects of various heating sources and other plasma operating parameters on overall plasma performance and may be used in future studies to inform the selection of plasma configurations that are more robust against power excursions.

Doctor of Philosophy / Department of Diagnostic Medicine/Pathobiology / Robert L. Larson / Bradley J. White / Utilization of data analytics allows for rapid and real-time decision making in the food animal production industry. The objective of my research was to implement and utilize different data analytic strategies in multiple sectors of the beef cattle industry in order to determine management, health, and performance strategies. A retrospective analysis using reproductive and genomic records demonstrated that a bull will sire a larger number of calves in a multiple sire-pasture compared to other bulls in the same pasture. A further study was performed to determine if behavior differences existed among bulls in a multiple-sire pasture, and the ability of accelerometers to predict breeding behaviors. Machine learning techniques used classifiers on accelerometer data to predict behavior events lying, standing, walking, and mounting. The classifiers were able to accurately predict lying and standing, but walking and mounting resulted in a lower predictable accuracy due to the extremely low prevalence of these behaviors. Finally, a new form of meta-analysis to the veterinary literature, a mixed treatment comparison, was able to accurately identify differences in metaphylactic antimicrobials on outcomes of bovine respiratory disease morbidity, mortality, and retreatment morbidity. The meta-analysis was not successful in determining the effects of metaphylactic antimicrobials on performance outcomes.

Social media has changed the way that we create, use, and disseminate information and presents an unparalleled opportunity to gather large-scale data on the networks, behaviors, and opinions of individuals. This dissertation focuses on the role of social media and social networks in recruitment, examining the complex interactions between offline recruiting activities, online social media, and recruiting outcomes. Specifically, it explores how the information college football recruits reveal about themselves online is related to their decisions as well as how this information can diffuse and influence the decisions of others. Recruitment occurs in many contexts, and this research draws comparisons between college football and personnel recruiting. This work is one of the first large-scale studies of social media in college football recruiting, and uses a unique dataset that is both broad and deep, capturing information about 2,644 recruits, 682 schools, 764 coaches, and 2,397 current college football players and tracking offline and online behavior over six months. This dissertation comprises three case studies corresponding to the major decisions in the football recruiting cycle—the coach’s decision to make a scholarship offer, the athlete’s decision to commit, and the athlete’s decision to decommit. The first study investigates the relationship between a recruit’s social media use and his recruiting success. Informed by previous work on impression management in personnel recruitment, I construct logistic classifiers to identify self-promotion and ingratiation in 5.5 million tweets and use regression analysis to model the relationship between tweets and scholarship offers over time. The results indicate that tweet content predicts whether an athlete will receive a new offer in the next month. Furthermore, the level of Twitter activity is strongly related to recruiting success, suggesting that simply possessing a social media account may offer a significant advantage in terms of attracting coaches’ attention and earning scholarship offers. These findings underscore the critical role of social media in athletic recruitment and may benefit recruits by informing their branding and communication strategies. The second study examines whether a recruit’s social media activity presages his college preferences. I combine data on recruits’ college options, recruiting activities, Twitter connections, and Twitter content to construct a logistic classifier predicting which school a recruit will select out of those that have offered him a scholarship. My results highlight the value of social media data—especially the hashtags posted by the athlete and his online social network connections—for predicting his commitment decision. These findings may prove useful for college coaches seeking innovative methods to compete for elite talent, as well as assisting them in allocating recruiting resources. The third study focuses on athletic turnover, i.e., decommitments. I construct a logistic classifier to predict the occurrence of decommitments over time based on recruits’ college choices, recruiting activities, online social networks, and the decommitment behavior of their peers. The results further underscore the power of online social networks for predicting offline recruiting outcomes, giving coaches the tools to better identify vulnerable commitments.

Operational support as an area of process mining aims to predict the temporal performance of individual cases and the overall business process. Although seasonal effects, delays and performance trends are well-known to exist for business processes, there is up until now no prediction model available that explicitly captures this. In this paper, we introduce time series Petri net models. These models integrate the control flow perspective of Petri nets with time series prediction. Our evaluation on the basis of our prototypical implementation demonstrates the merits of this model in terms of better accuracy in the presence of time series effects.

En la actualidad, las empresas manejan una gran cantidad de información, el cual era inimaginable años atrás, la capacidad de recolectarla es muy impresionante. En consecuencia, para varias empresas esta información se ha convertido en un tema difícil de manejar. Diariamente, las empresas sea del sector, tipo o tamaño que sea, toman decisiones, las cuales la mayoría son decisiones estratégicas que pueden afectar el correcto funcionamiento de la empresa. Es aquí, donde ingresa una de las herramientas más mencionadas en el área de TI: Business Intelligence, este término se refiere al uso de datos en una empresa para facilitar la toma de decisiones, explotar su información, y mejor aún, plantear o predecir escenarios a futuro. El presente trabajo permitirá al área de Marketing de la empresa Iasacorp International, obtener información sobre el comportamiento y hábitos de compra de los clientes, mediante técnicas de minería de datos como Árbol de Decisión y técnicas de análisis predictivo, la cual ayudará a la toma de decisiones para establecer estrategias de venta de las líneas (bisutería, complementos de vestir, accesorios de cabello, etc.) que maneja la empresa y de las próximas compras. De acuerdo a lo planteado anterior mente, la implementación de este tipo de sistemas de información ofrece a la empresa ventajas competitivas, permite a la gerencia analizar y entender mejor la información y por consecuencia tomar mejores decisiones de negocio. At present, companies handle a lot of information, which was unimaginable years ago, the ability to collect it is very impressive. Consequently, for many companies this information has become a difficult issue to handle. Due to the large volume of information we have, instead of being useful you can fall in a failed attempt to give proper use. Every day, companies in any sector, type or size, make decisions, most of which are strategic decisions that may affect the proper functioning of the company. It´s here, where we talk about the most mentioned tools in the area of IT: Business Intelligence, this term refers to the use of data in an enterprise to facilitate decision-making, exploit their information, and better yet, raise or predict scenarios future. This work will allow the area Iasacorp Marketing Company International, information on the behavior and buying habits of customers, through predictive analysis techniques, which will help the decision to establish sales strategies lines (jewelry, clothing, hair accessories, etc.) that manages the company and nearby shopping. According to the points made above, the implementation of such information systems offers companies competitive advantages, allows management to better analyze and understand information and consequently make better business decisions.