TimeGPT is a groundbreaking development in time series analysis, as it is the first foundation model capable of accurately predicting diverse datasets that were not seen during training. In our study, we compare TimeGPT with established statistical, machine learning, and deep learning methods, and the results clearly indicate that TimeGPT outperforms these approaches in terms of performance, efficiency, and simplicity. This suggests that insights from other domains of artificial intelligence can be successfully applied to time series analysis. The implications of large-scale time series models like TimeGPT are immense, as they have the potential to democratize access to precise predictions and reduce uncertainty by leveraging the advancements in deep learning.
https://arxiv.org/abs/2310.03589