Predictive Analytics: With Time-series Analyses And Generalized Linear Modeling

Predictive Analytics uses data to forecast future outcomes. It blends statistical theory and domain expertise to support decision-making in business, engineering, and healthcare. This text presents Predictive Analytics as a rigorous science with three pillars of Statistical Inference, Time-Series Analyses, and Generalized Linear Models (GLMs). Inference provides the foundation for model validity- ensuring predictions are not just correlations but statistically significant, while time-series models decode temporal data and forecast future values (demand, stock prices, environmental extremes) up to a certain accuracy level. We discuss how theoretical statistics and probability distributions are developed to algorithmic solution and mathematical methods, then employed in diverse domains of data intelligence and risk analytics. Application ranges from helping quality engineers to determine whether a process deviation is random or systematic, assisting environmentalists to forecast pollutant concentrations, and evaluate natural disaster levels. To decision making in insurance, actuaries may utilize Bayesian inference and GLMs with Poisson, gamma and binomial to model mortality rates.