Time series forecasting is a method used to predict future values based on previously observed data points collected over time. This technique is commonly applied in various fields, such as finance, weather forecasting, and sales analysis, where understanding trends and patterns is crucial. By analyzing historical data, models can identify seasonal variations and cyclical trends, helping businesses and organizations make informed decisions. The process typically involves using statistical models or machine learning algorithms to analyze the time series data. Popular methods include ARIMA (AutoRegressive Integrated Moving Average) and Exponential Smoothing. These models help generate forecasts that can guide strategic planning and resource allocation.