An Introduction to Time Series Analysis

This article presents a comprehensive introduction to time series analysis, outlining its fundamental principles, real-world relevance, and core methodologies. Emphasising both statistical rigour and practical application, the article covers the essential components of a time series—trend, seasonality, and noise—while distinguishing between stationary and non-stationary processes. It introduces key analytical techniques, including autocorrelation, decomposition, and various model classes such as AR, MA, ARMA, ARIMA, and SARIMA. Through clear exposition and illustrative examples, the article equips readers with a foundational understanding necessary to engage with time-dependent data in fields ranging from economics and finance to climate science and engineering.