Forecasting expertise may shift towards approaches and algorithms for evaluating accuracy and model fairness. We’ve seen this shift in the large language model (LLM) space, where organizations looking to incorporate them need to validate models and ensure they’re free from systematic bias. We’ll need to do that same work in forecasting as it becomes easier to employ forecasting models in more settings.
As with LLMs, we will see a growing family of forecasting models with different strengths and weaknesses (performance, size, speed, specialization).
At the time of writing, the number of foundation time series models is still limited but growing fast. As the field evolves, we’ll start to see more specialized models, for specific industries and purposes, to handle specific data types or to give users finer control to balance speed and accuracy, depending on what’s important in each task.
It won’t just be the models that will specialize but also the options and UI for interacting with them. The variety of options available for improving accuracy, the readily available exogenous variables, and how users access the models via code or interface will also be differentiators.
