Understanding Which Level of Predictive Forecasts You Need
Entities help you create your predictive forecasts at different levels, depending on your business needs. They can also help you detect performance gaps in your predictive models in some cases.
Entities are subsets of your predictive forecasts that are calculated independently from a combination of one to five dimensions. Each entity can be seen as an individual predictive forecast. These individual predictive forecasts can be agreggated at a higher level if needed.
The level you need for yourr predictive forecasts depends on the insights you want to get from your predictive forecasts and the data available in your source planning model. You can create entities by combining up to 5 dimensions for varying levels of high-detail predictive forecasts. You can also work without entities to keep your predictive forecasts high-level.
Let’s take a car sales scenario to explore in more detail how entities can help you find the level of predictive forecasts you need. Your company wants insights on future car sales. The company sells five car brands across six countries. You have five months of data available in your source planning model, wand you want two months of predictive forecasts.
Predictive forecasts without entities can give you insights on general trends across your data source, such as future car sales across all countries and brands.
In this case, you use car sales as your signal, Date as your date dimension and you train your predictive model.
You get a single predictive model that generates two car sales predictive forecasts, one for June and one for July.
This high-level forecasting of the company’s future car sales is useful if you just need an overview without looking into subset-specific trends.
Predictive forecasts using entities with multiple dimensions can give you insights on trends within and between several subsets of your data source such as car sales per brand and country.
In this case, you keep car sales as your signal and Date as your date dimension. You add Brand and Country dimensions as entities and train your predictive model.
You get thirty predictive models that are calculated independently. The thirty predictive models generate sixty predictive forecasts if all combinations have data available. This high-detail forecasting approach is useful if you need to focus on several subsets such as how one brand performs in a given country or how one country performs by brand compared to other countries. The sixty predictive forecasts can still be aggregated at a higher level by Country, Brand or Date if needed.
Predictive forecasts using entities with one dimension can give you insights on trends within one subset of your data source such as car sales per country
In this case, you keep car sales as your signal and Date as your date dimension. You add the Country dimension as entity and train your predictive model.
You get six predictive models (one per country) that are calculated independently. Twelve predictive forecasts are generated if all combinations of Country and Date are possible.
These twelve predictive forecasts can also be aggregated at a higher level by Country or Date if needed. This mid-level forecasting approach is useful when you need to focus on trends and relationships in one specific subset such as how each country will perform individually and compared to other countries.
Entities are a useful tool to tailor the level of your predictive forecasts to your different business needs. You may need to try a few forecasting combinations to reach the level of accuracy you want.