This monograph investigates the mathematical and statistical methodologies required to generate accurate short-term forecasts for industrial and operational applications. The authors, drawing on their expertise in applied statistics, present a rigorous framework for time-series analysis and predictive modeling. By focusing on the transition from raw data to actionable projections, the text establishes a systematic approach for minimizing error in volatile environments.

Experts recognize this work as a foundational text for understanding the classical statistical underpinnings of short-term forecasting. Readers frequently note the technical density of the prose, which requires a solid background in calculus and probability theory to fully grasp the presented models.