Applying zero-shot forecasting with standard machine learning models
The rise of Generative AI and Large Language Models (LLMs) has fascinated the entire world initializing a revolution in various fields. While the primary focus of this kind of technology has been on text sequences, further attention is now being given to expanding their capabilities to handle and process data formats beyond just text inputs.
Like in most AI areas, time series forecasting is also not immune to the advent of LLMs, but this may be a good deal for all. Time series modeling is known to be more like an art, where results are highly dependent on prior domain knowledge and adequate tuning. On the contrary, LLMs are appreciated for being task-agnostic, holding enormous potential in using their knowledge to solve variegated tasks coming from different domains. From the union of these two areas, the new frontier of time series forecasting models can be born which in the future will be able to achieve previously unthinkable results.
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