The forecasting process, illustrated in the following diagram, represents a typical process flow that can be applied to both time series and other models. There isn't a stage in the process that is more important than others. However, a good understanding of the business problem should be the building block of any modeling process:

Let us walk through the process of forecasting:

1. Define the business problem and state what the model expects to achieve.
2. Collect data from various sources and systems and transform it to clean data.
3. Plot the data, run statistical tests, fit the model, and interpret the results.
4. Validate the model by using a holdout sample.
5. Obtain a model sign after it meets the technical and the business requirements. Integrate it into the business process.

Most modelers spend a disproportionate amount of time on data, modeling, and validation tasks. At times, the business problem gets modified by the addition or deletion of scope. This causes problems for the modeler, as modeling times seldom get adjusted to deal with a retrospective change in scope. Another aspect related to the business problem that isn't explicitly mentioned in the forecasting process is the interaction with the subject matter experts (SMEs). The back and forth dialogue with the SMEs adds quality and perspective to the modeling process. This can also be a challenging task, given the timelines involved. However, a conversation during the modeling process can ensure that there are no surprises for anyone involved in the project. This aids in the integration of the model into the business process.

The larger the organization, the greater the risk that the modeler will not be involved in the integration of the modeling output. Modeling becomes a specialized unit in large organizations, and the modeler is involved only indirectly in the implementation. However, feedback processes do exist, and the modeler should request an update and ensure that the model is performing as expected. In some of the models, where there is an operational need to keep running the model, a monitoring aspect is also involved. This is a more formal process, where interaction with the business is higher than with a model that was built as a one-off.