Forecasting, the process of predicting future events, is a fundamental element of many disciplines, including economics, meteorology, and social sciences. This text provides an overview of time series analysis, a powerful technique for understanding and forecasting data that evolves over time. The document explores the components of a time series, including trend, cyclical, seasonal, and irregular components. It also outlines quantitative forecasting methods, such as moving averages, exponential smoothing, and autoregressive models, which utilize historical data to make predictions. Finally, the text delves into stationarity, a crucial property for time series data, and discusses the ARIMA model, which is widely used for forecasting non-stationary time series.