Anaplan's Statistical Forecasting model example allows you to load historical data and generate a forecast using various statistical algorithms. The model includes over 30 of these algorithms across four different overarching forecasting methods: Basic and Intermittent Demand, Curve Fit, Smoothing, and Seasonal Smoothing.

Not only does this model generate statistical forecasts based on historical data but it also analyzes which algorithm would have best fit that data. This approach provides you with a suggestion of which method may be the most accurate to use for future periods.

Statistical Forecasting Algorithms

  • Over 30 different statistical algorithms, including simple exponential smoothing, multiplicative decomposition, and winter’s additive.
  • Interactive forecasting dashboard that allows for customizing forecast algorithms and assumptions.

Seasonality Options

  • Multiple Seasonality selection options that include find each product’s unique seasonality, a brand-level composite seasonality, and custom user defined options.

Outlier Identification

  • Outlier identification and exclusion based on user defined parameters such as standard deviation or the Inter-Quartile Range (IQR).

Forecast Accuracy

  • Forecast accuracy measurement with techniques such as Mean Absolute Percent Error (MAPE).

Dynamic time scale

End-of-life modeling

Business function

Sales: Sales Forecasting


All Industries, Healthcare, Hospitality


190 MB