Predictive Modeling

Generative AI has made waves in various fields, but its ability to predict future events has been notably lacking. Traditional large language models (LLMs) like OpenAI’s GPT and Google’s Gemini have struggled with forecasting due to their training on unstructured data, which often lacks the numerical and chronological context necessary for accurate predictions. However, the emergence of tabular foundation models is set to change the game.

These new models are specifically designed to work with structured data, allowing them to make more informed forecasts. By focusing on numerical data and time series, tabular foundation models can provide businesses with insights into trends like customer churn and product popularity more effectively than their predecessors. This shift could streamline the predictive analytics process, reducing the need for separate models for each forecasting factor.