  In this paper, we introduce TimeGPT, the first foundation model for time
series, capable of generating accurate predictions for diverse datasets not
seen during training. We evaluate our pre-trained model against established
statistical, machine learning, and deep learning methods, demonstrating that
TimeGPT zero-shot inference excels in performance, efficiency, and simplicity.
Our study provides compelling evidence that insights from other domains of
artificial intelligence can be effectively applied to time series analysis. We
conclude that large-scale time series models offer an exciting opportunity to
democratize access to precise predictions and reduce uncertainty by leveraging
the capabilities of contemporary advancements in deep learning.
