Forecasting involves using statistical models to predict future values of a variable, such as sales or economic indicators, by analyzing historical patterns. Its importance lies in aiding planning and decision-making, guiding strategies, budgeting resources, and managing inventory.
Forecasting finds application across various sectors: governments use it for policy planning, retail for inventory management, transportation for optimizing schedules, and finance for predicting market movements.
The process typically involves data collection, analysis, model selection, and generating forecasts. The choice of model depends on the data and goals, ranging from simple trend extensions to complex computer-based predictions. In our project, one method employed is SARIMA, or Seasonal AutoRegressive Integrated Moving Average, specifically suited for time series data with seasonal patterns like economic indicators.