Time series forecasting presents a dynamic challenge marked by the continual evolution of temporal data patterns. Even though several machine learning models have been used to tackle this task, it's widely acknowledged that there isn't a single model universally applicable across all scenarios and persistently effective over time. Hence, models that excel in one time interval may prove inadequate in another. This stems from the realization that various models exhibit distinct "Regions of Competence" (RoCs) within the time series, signifying their superior performance in specific temporal contexts. Consequently, the need arises for a well-informed and dynamic process of selecting models in real-time to address the ever-changing characteristics inherent in time series data. This can be achieved by implementing mechanisms capable of discerning the evolving nature of the data, recognizing the RoCs associated with different models, and choosing the model most likely to yield accurate predictions for the present time period. This adaptability and agility in model selection are pivotal for sustaining the accuracy and relevance of time series forecasting in the face of temporal fluctuations. The Regions of Competence (RoCs) serve a dual purpose: they not only direct the model selection process but also enrich the narrative explaining why specific models were preferred during distinct time intervals or instants. This dynamic adds depth to our comprehension of the reasoning behind the decisions in time series forecasting, significantly enhancing transparency and making the decision-making process more accessible and comprehensible to stakeholders.