Developing Machine Learning and Time-Series Analysis Methods with Applications in Diverse Fields

Abstract

This dissertation introduces methodologies that combine machine learning models with time-series analysis to tackle data analysis challenges in varied fields. The first study enhances the traditional cumulative sum control charts with machine learning models to leverage their predictive power for better detection of process shifts, applying this advanced control chart to monitor hospital readmission rates. The second project develops multi-layer models for predicting chemical concentrations from ultraviolet-visible spectroscopy data, specifically addressing the challenge of analyzing chemicals with a wide range of concentrations. The third study presents a new method for detecting multiple changepoints in autocorrelated ordinal time series, using the autoregressive ordered probit model in conjunction with a genetic algorithm. This technique is applied to the air quality index data for Los Angeles, aiming to detect significant changes in air quality over time.