What is the purpose of partitioning topics in Kafka?

Hierarchical forecasting is a method of predicting data organized in a hierarchy (e.g., sales by regions, countries, and globally). The goal is to obtain forecasts at different levels of the hierarchy.

Reconciliation is the process of adjusting forecasts at different levels to ensure they are consistent with each other. For example, the sum of forecasts for regions should match the forecast for the entire country.

Main approaches to reconciliation:

• Bottom-up: forecasts are made at the lowest level and then aggregated upwards.
• Top-down: forecasts are made at the top level and then distributed down.
• Middle-out: combines both approaches.
• Statistical methods (e.g., MinT) that minimize errors during reconciliation.

Thus, hierarchical forecasting and reconciliation help improve the accuracy and consistency of forecasts in complex hierarchical data structures.