Time Series Analysis and Forecasting

Time Series Forecasting: An Introduction provides a systematic and learner-friendly overview of the principles, methods, and applications of time-dependent data analysis. The book begins with the fundamentals of time series data, including data types, components, visualization techniques, and preprocessing methods such as smoothing, decomposition, and stationarity testing.
It introduces classical forecasting models such as moving averages, exponential smoothing, autoregressive (AR), moving average (MA), and ARIMA models, explaining their assumptions and use cases. The book also explores seasonal models, multivariate time series, and basic state-space approaches. Evaluation techniques, including forecast accuracy measures and validation strategies, are discussed to help readers assess model performance effectively.
In addition, the text offers an introduction to modern approaches, including machine learning-based forecasting methods, highlighting their advantages and limitations. Real-world examples from finance, economics, healthcare, weather forecasting, and industrial operations illustrate practical applications.
Designed for undergraduate and postgraduate students, data analysts, and researchers, this book balances theory with practice. It equips readers with the foundational skills needed to analyze temporal data, build forecasting models, and interpret results responsibly in academic and applied settings.