This chapter provides a thorough introduction to time series analysis, a crucial component of statistical analysis and forecasting. It starts with the principles of time series and the many objects used to describe time-varying data. The chapter delves into trends and seasonal variations before moving on to strategies for breaking down time series into constituent components. Seasonal models and strategies for smoothing and decomposition are thoroughly examined. Autocorrelation and partial autocorrelation are discussed, as well as how to interpret ACF and PACF charts. The chapter also discusses correlation analysis, exponential smoothing approaches, and the Holt-Winters method for advanced forecasting. By the end of this chapter, readers will have a thorough understanding of time series analysis as well as practical abilities for analyzing and forecasting.