Forecasting Methods

Forecasting is predicting the future trend of a variable accurately based on its historical trends and possible future events. Forecasting is important in business for strategic planning, scheduling production and resources, and setting goals. Forecasting involves predicting uncertain future events. This requires expertise in choosing the right forecast method in each situation and applying the chosen method to give accurate predictions. A forecasting system should evaluate and refine different forecasting methods.

Applications of forecasting

Businesses use forecasting methods to predict sales and demand for their goods or services. With such predictions, business can schedule production to meet the expected demand and avoid wastage of production resources. Businesses can estimate income from new investments and risks associated with such investments. Forecasting is also used to predict the responses to marketing campaigns and strategies. Hence, businesses allocate resource to their marketing campaigns efficiently. Forecasting helps businesses schedule their recruitment processes and purchases of raw materials and equipment.

In macroeconomics, forecasting is used to predict change in GDP, unemployment rates, population growth rates, and inflation. Such predictions are useful in preparing national budgets and in policy development.

Common Forecasting Methods

Business and national governments have many forecasting methods to choose from depending on the type of data in consideration. Extrapolative forecasting methods include simple moving averages such as time series, exponential smoothing, and autoregressive moving average. Exponential smoothing methods consider the trend and seasonality of data. Autoregressive methods can indicate autocorrelations in data and are mainly used for nonvolatile historical data. Explanatory variable methods of forecasting include regression analysis, econometrics, predictive modeling and artificial neural networks.

Regression analysis gives correlations between dependent and independent variables and maybe used to analyze trends in historical data. Regression analysis are not recommended in business forecasting. Econometric modeling uses simultaneous equations to outline relationships between economic variables. Predictive models derive all variables in data that could influence future patterns and behavior. Artificial neural networks use nonlinear correlations between variables to predict future data trends.

Simulation methods of forecasting include cell-based, systems dynamics and multi-agent simulation models. Cell-based simulation methods include homogenous units or cells of large systems. Systems dynamics on the other hand include whole systems and consider systemic trends over time. Judgmental methods such as the Delphi methods rely on the intuition of an expert instead of statistical trends of data. Bayesian forecasting methods combine statistical data analysis with human judgment or intuition to predict future. Such composite methods are preferred in business forecasting instead of relying on one forecasting method.

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