Forecasting Methods

Introduction: Forecasting Methods

Forecasting methods are techniques used to predict future outcomes based on historical data and analysis. They are widely applied in business, economics, supply chain management, and many other fields to support planning and decision-making.

Background

Forecasting has been used for centuries, from predicting harvests to economic cycles. Modern forecasting methods range from simple statistical tools to advanced machine learning models. The choice of method depends on data availability, complexity, and required accuracy.

Key Elements / Features

Forecasting methods are usually divided into two main categories:

• Qualitative methods: Based on expert judgement or market research. Examples include the Delphi method, focus groups, and scenario planning.
• Quantitative methods: Use mathematical models and historical data. Examples include:
  • Time Series Models (ARIMA, SARIMA, Exponential Smoothing).
  • Causal Models (regression analysis, econometric models).

Key features of good forecasts include accuracy, reliability, and the ability to adapt to changing conditions.

Applications / Examples

• Business: Predicting product demand to optimise inventory and staffing.
• Finance: Forecasting stock prices, interest rates, or currency exchange rates.
• Healthcare: Anticipating patient numbers to allocate resources.
• Weather: Using historical climate data to forecast temperatures and rainfall.

For example, a retailer may use time series forecasting to predict sales peaks during holidays, while also applying qualitative judgement to account for new fashion trends.

Relevance / Impact

Forecasting methods help organisations reduce uncertainty and prepare for the future. They support strategic planning, risk management, and resource allocation. The integration of both qualitative and quantitative approaches often leads to the most reliable results.

See also

• ARIMA
• SARIMA
• Time Series Analysis
• Box-Jenkins Methodology