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  2. Bass diffusion model - Wikipedia

    en.wikipedia.org/wiki/Bass_diffusion_model

    The Bass model has been widely used in forecasting, especially new product sales forecasting and technology forecasting. Mathematically, the basic Bass diffusion is a Riccati equation with constant coefficients equivalent to Verhulst—Pearl logistic growth. In 1969, Frank Bass published his paper on a new product growth model for consumer ...

  3. Mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_percentage_error

    It is a measure used to evaluate the performance of regression or forecasting models. It is a variant of MAPE in which the mean absolute percent errors is treated as a weighted arithmetic mean. Most commonly the absolute percent errors are weighted by the actuals (e.g. in case of sales forecasting, errors are weighted by sales volume). [3]

  4. Demand forecasting - Wikipedia

    en.wikipedia.org/wiki/Demand_forecasting

    Demand forecasting, also known as demand planning and sales forecasting (DP&SF), [1] involves the prediction of the quantity of goods and services that will be demanded by consumers or business customers at a future point in time. [2]

  5. Tracking signal - Wikipedia

    en.wikipedia.org/wiki/Tracking_signal

    Forecasts can relate to sales, inventory, or anything pertaining to an organization's future demand. The tracking signal is a simple indicator that forecast bias is present in the forecast model. It is most often used when the validity of the forecasting model might be in doubt.

  6. Forecasting - Wikipedia

    en.wikipedia.org/wiki/Forecasting

    Forecasting is the process of making predictions based on past and present data. Later these can be compared with what actually happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis.

  7. Symmetric mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Symmetric_mean_absolute...

    The following example illustrates this by applying the second SMAPE formula: Over-forecasting: A t = 100 and F t = 110 give SMAPE = 4.76%; Under-forecasting: A t = 100 and F t = 90 give SMAPE = 5.26%.

  8. Predictive analytics - Wikipedia

    en.wikipedia.org/wiki/Predictive_analytics

    Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, "Predictive analytics—Technology that learns from experience (data) to predict the future behavior of ...

  9. Volume projections - Wikipedia

    en.wikipedia.org/wiki/Volume_projections

    Volume projections enable marketers to forecast sales by sampling customer intentions through surveys and market studies. By estimating how many customers will try a new product, and how often they’ll make repeat purchases, marketers can establish the basis for such projections. . . . Projections from customer surveys are especially useful in ...