Time Series Analysis is a statistical technique used to analyze time-ordered data points. It helps in understanding underlying patterns, trends, and seasonal variations in data collected over time, such as economic indicators or weather data. By examining these patterns, analysts can make informed predictions about future values. This method is widely applied in various fields, including finance, economics, and environmental science. Techniques such as ARIMA (AutoRegressive Integrated Moving Average) and exponential smoothing are commonly used to model and forecast time series data, enabling better decision-making based on historical trends.