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  2. Demand forecasting - Wikipedia

    en.wikipedia.org/wiki/Demand_forecasting

    The type of model that is chosen to forecast demand depends on many different aspects such as the type of data obtained or the number of observations, etc. [10] In this stage it is important to define the type of variables that will be used to forecast demand. Regression analysis is the main statistical method for forecasting. There are many ...

  3. Forecasting - Wikipedia

    en.wikipedia.org/wiki/Forecasting

    Accurate forecasting will also help them meet consumer demand. The discipline of demand planning, also sometimes referred to as supply chain forecasting, embraces both statistical forecasting and a consensus process. Studies have shown that extrapolations are the least accurate, while company earnings forecasts are the most reliable.

  4. Economic forecasting - Wikipedia

    en.wikipedia.org/wiki/Economic_forecasting

    Methods of forecasting include Econometric models, Consensus forecasts, Economic base analysis, Shift-share analysis, Input-output model and the Grinold and Kroner Model. See also Land use forecasting , Reference class forecasting , Transportation planning and Calculating Demand Forecast Accuracy .

  5. Pricing science - Wikipedia

    en.wikipedia.org/wiki/Pricing_science

    Forecasting methods generally fall into the class of methods known as time series methods, primarily exponential smoothing, or causal methods, where price is taken to be (one of) the causal factors. In pricing science applications, it is necessary to produce forecasts of demand at the level of granularity at which pricing decisions are made.

  6. Predictive analytics - Wikipedia

    en.wikipedia.org/wiki/Predictive_analytics

    Predictive analytics, or predictive AI, encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.

  7. Delphi method - Wikipedia

    en.wikipedia.org/wiki/Delphi_method

    Quantitative methods produced errors of 10–15%, and traditional unstructured forecast methods had errors of about 20%. (This is only one example; the overall accuracy of the technique is mixed.) The Delphi method has also been used as a tool to implement multi-stakeholder approaches for participative policy-making in developing countries.

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