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This post was co-authored with Rafael Guedes.

Forecasting is one of the core domains of Artificial Intelligence (AI) in academic research and industrial applications. In fact, it is probably one of the most ubiquitous challenges we can find across all industries. Accurately predicting future sales volumes and market trends is essential for businesses to optimize their planning processes. This includes enhancing contribution margins, minimizing waste, ensuring adequate inventory levels, optimizing the supply chain, and improving decision-making overall.

Developing a forecast model represents a complex and multifaceted challenge. It requires a deep understanding of State-Of-The-Art (SOTA) forecasting methodologies and the specific business domain to which they are applied. Furthermore, the forecast engine will act as a critical infrastructure within an organization, supporting a broad spectrum of processes across various departments. For instance:

Recent advancements in forecasting have also been shaped by the successful development of foundational models across various domains, including text (e.g., ChatGPT), text-to-image (e.g., Midjourney), and text-to-speech (e.g., Eleven Labs). The wide

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TimeGPT vs TiDE: Is Zero-Shot Inference the Future of Forecasting or Just Hype? - Towards Data Science