Explore How Google’s Latest AI Model Delivers Zero-Shot Forecasting Accuracy Using Over 307 Billion Data Points
This post was co-authored with Rafael Guedes.
Forecasting is one of the most important use cases across all industries. One example is the retail industry. Several planning activities require predicting capabilities, and these contribute to optimizing margin, e.g., financial, production, or workforce planning. This can impact stock management, for instance, waste and leftovers or stockouts, customer service levels, and overall decision-making.
Developing an accurate forecasting model to support the above-mentioned processes requires a deep understanding of state-of-the-art (SOTA) forecasting methodologies. At the same time, it requires specific business domain knowledge to which they are applied. These two factors have been motivating the increasing interest in pre-trained models — they reduce the need for highly custom setups. Adding that motivation to the success of large pre-trained models in the Natural Language Processing (NLP) community, a.k.a. Large Language Models (LLMs), we have a research path with many contributors.
Theoretically, we know several similarities between language and time series tasks, such as the fact that the data is sequential. On the other hand, one key difference is…