This chapter provides an introduction to time series analysis and statistical methods used to describe and interpret them. It begins with an overview of time series, explaining their significance and presenting real-world examples from fields such as economics, meteorology, and data science. The chapter then discusses key statistical analysis techniques, distinguishing between univariate, bivariate, and multivariate approaches. Next, it covers descriptive statistics used in time series analysis, including measures such as the mean, median, variance, standard deviation, skewness, and kurtosis. These metrics help summarize the distribution and variability of time series data. The chapter also explores the components of a time series, focusing on trends and seasonality. It introduces methods for estimating periodicity and techniques for detrending and de-seasoning time series data, such as: Finally, the chapter applies these techniques to real-world time series datasets, demonstrating practical implementation. This chapter serves as a fundamental introduction to time series analysis, equipping readers with essential statistical concepts and techniques for preprocessing data before applying advanced forecasting and modeling methods.