Stock Price Prediction using Adaptive Time Series Forecasting and Machine Learning Algorithms

In this thesis, ARIMA model, Long Short Term Memory (LSTM) model and Extreme Gradient Boosting (XGBoost) models were developed to predict daily adjusted close price of selected stocks from January 3, 2017 to April 24, 2020. Daily stock price data includes columns of open, close, adjusted close, high, low and volume. In ARIMA and LSTM models, the only features we used as model inputs were previous N days’ stock prices. Prediction on day N+1 was calculated based on previous N values. RMSE and MAPE were calculated from this rolling forecast and the actual price in the test dataset. Optimal parameters were selected to be the setting that yielded the lowest RMSE score. Residuals diagnostic was performed to check model assumption for the final ARIMA model. In XGBoost model, feature engineering was used to create two additional features from open, close, high and low price. Same with LSTM model, previous N days features were used as features in day N+1 for prediction. In both LSTM and XGBoost models, training dataset was scaled for model fitting. Features and output from cross-validation and test dataset were scaled too based on previous N days’ values. The prediction results were then reverted back to original scale before calculation of RMSE and MAPE scores. In conclusion, looking at the prediction versus actual stock price plot for each stock and their RMSE and MAPE scores, all three models produced good forecast of next day’s stock price. However, during the time with great volatility, the lag between forecast value and actual value is more noticeable. In our models, historical N days stock price on its own could provide a relatively accurate prediction on N+1 day’s stock price. In XGBoost model particularly, we found out that N=2 provided better RMSE and MAPE(%) results than other larger values of N (previous N days). As N gets larger, prediction accuracy got lower in XGBoost. In XGBoost feature importance analysis, the most important factor to today’s stock price is its price yesterday. Although the final ARIMA model achieved the lowest RMSE score, grid search for one-step ARIMA forecast model parameters took the longest computing time, while XGBoost model with the second lowest RMSE score required the least time for parameter tuning and forecast calculation

Robust Magnetic Resonance Imaging of Short T2 Tissues

Tissues with short transverse relaxation times are defined as ‘short T2 tissues’, and short T2 tissues often appear dark on images generated by conventional magnetic resonance imaging techniques. Common short T2 tissues include tendons, meniscus, and cortical bone. Ultrashort Echo Time (UTE) pulse sequences can provide morphologic contrasts and quantitative maps for short T2 tissues by reducing time-of-echo to the system minimum (e.g., less than 100 us). Therefore, UTE sequences have become a powerful imaging tool for visualizing and quantifying short T2 tissues in many applications. In this work, we developed a new Flexible Ultra Short time Echo (FUSE) pulse sequence employing a total of thirteen acquisition features with adjustable parameters, including optimized radiofrequency pulses, trajectories, choice of two or three dimensions, and multiple long-T2 suppression techniques. Together with the FUSE sequence, an improved analytical density correction and an auto-deblurring algorithm were incorporated as part of a novel reconstruction pipeline for reducing imaging artifacts. Firstly, we evaluated the FUSE sequence using a phantom containing short T2 components. The results demonstrated that differing UTE acquisition methods, improving the density correction functions and improving the deblurring algorithm could reduce the various artifacts, improve the overall signal, and enhance short T2 contrast. Secondly, we applied the FUSE sequence in bovine stifle joints (similar to the human knee) for morphologic imaging and quantitative assessment. The results showed that it was feasible to use the FUSE sequence to create morphologic images that isolate signals from the various knee joint tissues and carry out comprehensive quantitative assessments, using the meniscus as a model, including the mappings of longitudinal relaxation (T1) times, quantitative magnetization transfer parameters, and effective transverse relaxation (T2*) times. Lastly, we utilized the FUSE sequence to image the human skull for evaluating its feasibility in synthetic computed tomography (CT) generation and radiation treatment planning. The results demonstrated that the radiation treatment plans created using the FUSE-based synthetic CT and traditional CT data were able to present comparable dose calculations with the dose difference of mean less than a percent. In summary, this thesis clearly demonstrated the need for the FUSE sequence and its potential for robustly imaging short T2 tissues in various applications

Flow cytometry analyses of adipose tissue macrophages

Within adipose tissue, multiple leukocyte interactions contribute to metabolic homeostasis in health as well as to the pathogenesis of insulin resistance with obesity. Adipose tissue macrophages (ATMs) are the predominant leukocyte population in fat and contribute to obesity-induced inflammation. Characterization of ATMs and other leukocytes in the stromal vascular fraction from fat has benefited from the use of flow cytometry and flow-assisted cell sorting techniques. These methods permit the immunophenotyping, quantification, and purification of these unique cell populations from multiple adipose tissue depots in rodents and humans. Proper isolation, quantification, and characterization of ATM phenotypes are critical for understanding their role in adipose tissue function and obesity-induced metabolic diseases. Here, we present the flow cytometry protocols for phenotyping ATMs in lean and obese mice employed by our laboratory

MAGNETIC RESONANCE ELASTOGRAPHY FOR APPLICATIONS IN RADIATION THERAPY

Magnetic resonance elastography (MRE) is an imaging technique that combines mechanical waves and magnetic resonance imaging (MRI) to determine the elastic properties of tissue. Because MRE is non-invasive, there is great potential and interest for its use in the detection of cancer. The first part of this thesis concentrates on parameter optimization and imaging quality of an MRE system. To do this, we developed a customized quality assurance phantom, and a series of quality control tests to characterize the MRE system. Our results demonstrated that through optimizing scan parameters, such as frequency and amplitude, MRE could provide a good qualitative elastogram for targets with different elasticity values and dimensions. The second part investigated the feasibility of integrating MRE into radiation therapy (RT) workflow. With the aid of a tissue-equivalent prostate phantom (embedded with three dominant intraprostatic lesions (DILs)), an MRE-integrated RT framework was developed. This framework contains a comprehensive scan protocol including Computed Tomography (CT) scan, combined MRI/MRE scans and a Volumetric Modulated Arc Therapy (VMAT) technique for treatment delivery. The results showed that using the comprehensive information could boost the MRE defined DILs to 84 Gy while keeping the remainder of the prostate to 78 Gy. Using a VMAT based technique allowed us to achieve a highly conformal plan (conformity index for the prostate and combined DILs was 0.98 and 0.91). Based on our feasibility study, we concluded that MRE data can be used for targeted radiation dose escalation. In summary, this thesis demonstrates that MRE is feasible for applications in radiation oncology

Obesity‐Related Hormones in Low‐Income Preschool‐Age Children: Implications for School Readiness

Mechanisms underlying socioeconomic disparities in school readiness and health outcomes, particularly obesity, among preschool‐aged children are complex and poorly understood. Obesity can induce changes in proteins in the circulation that contribute to the negative impact of obesity on health; such changes may relate to cognitive and emotion regulation skills important for school readiness. We investigated obesity‐related hormones, body mass index ( BMI ), and school readiness in a pilot study of low‐income preschoolers attending Head Start (participating in a larger parent study). We found that the adipokine leptin was related to preschoolers' BMI z ‐score, the appetite‐regulating hormones ghrelin and glucagon‐like peptide 1 ( GLP ‐1), and pro‐inflammatory cytokines typically associated with early life stress; and that some of these obesity‐related biomarkers were in turn related to emotion regulation. Future work should evaluate how obesity may affect multiple domains of development, and consider modeling common physiological pathways related to stress, health, and school readiness.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101799/1/mbe12034.pd

Simulation of Rolling Process of AZ31 Magnesium Alloy Sheet

AbstractTo understand more about the rolling process of an AZ31 magnesium alloy sheet and the difference of simulation results between 2D and 3D, the rolling experiment of AZ31 was carried out and some useful data were obtained, and then the rolling processes were simulated by DEFORM2D and 3D respectively. The simulation results are in good agreement with the experimental results based on selecting the correct parameters of stress-strain relationship of AZ31, the friction factor with or without lubricant and the interfacial heat transfer coefficient. The influences of rolling reduction, workpiece temperature and roller temperature on the rolling load and torque are discussed and the difference of simulation results between 2D and 3D is illustrated

Study on the mycorrhizal structure of common plants and rhizosphere AMF diversity of different plant communities in Central Province, Mongolia

With the dual destruction cased by man-made activities and natural causes, the biodiversity and ecosystem function of the prairie are reducing rapidly, which are manifest in such phenomenon as grassland desertification, sharp reduction in wetland, soil quality degradation, erosion of soil by wind, rain and watersheds. This condition restricts the development level of Mongolia's financial status and production forces, and so the protection and utilization of biodiversity resources are extremely important and harbor no delay. Arbuscular mycorrhizal fungi (AMF) has a broad distribution and species diversity, it also has very important functions of maintaining material circulation in ecosystems, improving ecosystem productivity, and ensuring ecological restoration. We selected different plant communities and the common plants in the Tuv aimag (Central province) of Mongolia to study the correlation between species diversity, genetic diversity and AM fungi distribution with physical and chemical properties of soil

Methodological considerations for observational coding of eating and feeding behaviors in children and their families

Abstract Background Behavioral coding of videotaped eating and feeding interactions can provide researchers with rich observational data and unique insights into eating behaviors, food intake, food selection as well as interpersonal and mealtime dynamics of children and their families. Unlike self-report measures of eating and feeding practices, the coding of videotaped eating and feeding behaviors can allow for the quantitative and qualitative examinations of behaviors and practices that participants may not self-report. While this methodology is increasingly more common, behavioral coding protocols and methodology are not widely shared in the literature. This has important implications for validity and reliability of coding schemes across settings. Additional guidance on how to design, implement, code and analyze videotaped eating and feeding behaviors could contribute to advancing the science of behavioral nutrition. The objectives of this narrative review are to review methodology for the design, operationalization, and coding of videotaped behavioral eating and feeding data in children and their families, and to highlight best practices. Methods When capturing eating and feeding behaviors through analysis of videotapes, it is important for the study and coding to be hypothesis driven. Study design considerations include how to best capture the target behaviors through selection of a controlled experimental laboratory environment versus home mealtime, duration of video recording, number of observations to achieve reliability across eating episodes, as well as technical issues in video recording and sound quality. Study design must also take into account plans for coding the target behaviors, which may include behavior frequency, duration, categorization or qualitative descriptors. Coding scheme creation and refinement occur through an iterative process. Reliability between coders can be challenging to achieve but is paramount to the scientific rigor of the methodology. Analysis approach is dependent on the how data were coded and collapsed. Conclusions Behavioral coding of videotaped eating and feeding behaviors can capture rich data “in-vivo” that is otherwise unobtainable from self-report measures. While data collection and coding are time-intensive the data yielded can be extremely valuable. Additional sharing of methodology and coding schemes around eating and feeding behaviors could advance the science and field.https://deepblue.lib.umich.edu/bitstream/2027.42/140067/1/12966_2017_Article_619.pd