Modern Time Series Analysis with R Practical forecasting and impact estimation with tidy, reproducible workflows (Dr. Yeasmin Khandakar, Dr. Roman Ahmed)（Z-Library）

Author： Dr. Yeasmin Khandakar, Dr. Roman Ahmed

From the experience across industries, it has been realized that the challenge is not the availability of resources or tools. The challenges are two-fold: (i) a gap in translating business problems into a time-series context, (ii) awareness of time-series-based techniques that could solve business problems with less complexity and cost. This book explores the full evolution of modern forecasting, from classical statistical methods to machine/deep learning. It tackles high dimensional challenges such as hierarchical forecasting and multiple time series methods to ensure consistency across complex business structures. Beyond forecasting, the book investigates the “why” and “when” of data shifts by estimating causal impact. It discusses time-series based methodologies to analyze business intervention data where traditional A/B testing is not applicable. Readers will learn how to identify critical turning points through changepoint analysis and anomaly detection. Ultimately, the book will equip you with the rigorous methodology and technical command necessary to transform volatile temporal data into a cornerstone of evidence-based decision-making.