All the concepts needed to fully understand the project are described more generally in this section before going into the specifics of each recipe in the [flow documentation](article:17).

## Summary

1. [Data Model](article:8): To match the expected input datasets format, data preparation and feature engineering steps should be done in a separate project. This article describes the necessary format criteria to be met.

2. [Data Preparation](article:11): Data preparation steps in the solution include data normalization and lag values creation. This article details the techniques used in both cases. 

3. [Forecasts Creation](article:13): The solution creates two distinct forecasts from the data uploaded by the user: simple forecast and advanced forecast. This article provides detailed information about the approach and forecasting methods used. 

4. [Generative AI](article:50): From the analysis and forecasts produced by the project, a report is generated using LLMs.