While I don't have firsthand experience with these models, I recently discussed this topic with a friend who has used tree-based models like XGBoost for time series analysis. They noted that transformer-based architectures tend to yield decent performance on time series tasks with relatively little effort compared to tree models.
From what I understood, tree-based models can usually outperform transformers when given sufficient parameter tuning. However, models like TimeGPT offer decent performance without extensive tuning, making them an attractive option for quicker implementations.
From what I understood, tree-based models can usually outperform transformers when given sufficient parameter tuning. However, models like TimeGPT offer decent performance without extensive tuning, making them an attractive option for quicker implementations.