The process of estimating anticipated sales performance for a specified period using historical data. This estimation aids in task planning and setting standards.

Forecasting refers to the process of making educated predictions about the future based on past and present data [1]. It’s a crucial tool used in various fields to gain insights and make informed decisions.

Here’s a deeper dive into what forecasting entails:

• Leveraging Data: Forecasters rely on historical data, current trends, and relevant factors to estimate future outcomes. This data can come from sales figures, market research, weather patterns, or economic indicators, depending on the specific domain [2, 3].
• Statistical Methods and Expertise: A variety of statistical methods and models are employed to analyze data and identify patterns. Forecasters with domain expertise can then interpret these patterns and adjust for potential uncertainties to create a reliable forecast [1, 2].
• Applications Across Industries: Forecasting has widespread applications in business, finance, economics, supply chain management, weather prediction, and even scientific research [1]. Businesses use it to predict sales, manage inventory, and plan for future growth.
• Limitations and Revisions: It’s important to remember that forecasting is not an exact science. The future is inherently uncertain, and unforeseen events can significantly impact predictions. Forecasts should be viewed as estimates and are often revised as new information becomes available [2, 3].

Here are some of the common types of forecasting methods used:

• Time Series Analysis: This method analyzes historical data points over time to identify trends and seasonality, which can then be used to project future values [2].
• Econometric Models: These are complex statistical models that take into account various economic factors to forecast future economic conditions [1].
• Judgmental Forecasting: This method incorporates expert opinions and insights alongside historical data to arrive at a forecast, particularly useful when historical data is limited [3].