Causal Forecast

Businesses are constantly affected by externalities beyond the control of their organization.

  1. Weather affects sale of milk, bread and eggs at grocery stores
  2. Housing construction could foretell carpet sales
  3. Auto builds forecast could predict number of auto parts needed
  4. Industrial production increase might signal increased demand of consumables in future months
  5. Warm weather forecast for the weekend might mean higher hotdog sales for barbeques

Arkieva’s regression based causal forecasting engine enables business to analyze a wide variety of external factors to identify those that influence their business directly as leading indicators. These leading indicators are then used to predict the forecast of sales for the business.

Built on Arkieva’s attribute based data model, the causal forecasting engine allows users to factor in any leading indicators and calculate forecasts at any level of data hierarchy. Further, because this is a part of the statistical forecasting engine, causal methods can be used as one of the inputs when trying to determine the best-fit method.