- The main weakness of this paper is that it overclaims and underdelivers. In its current state, the study is not strong enough to claim the title of a "foundational model".
- The authors mention that they use datasets from diverse domains. However, out of the 12 datasets studied, 6 come from a single domain. The distribution of sampling frequencies of these datasets are also not diverse with 6 hourly datasets and a limited representation of other frequencies (and some popular frequencies completely missing).
- Another aspect that could have justified the term "foundational" is a diversity of tasks. However, the paper mostly focuses on the long-term forecasting tasks with limited discussion of other tasks. Importantly, the practically relevant task of short-term forecasting (e.g., Monash time series forecasting archive) gets very less attention.
- The claim _Most existing forecasting models were designed to process regularly sampled data of fixed length. We argue that this restriction is the central reason for poor generalisation in time series forecasting_ has not been justified convincingly.
- The visualizations are poorly done and confusing for a serious academic paper. Please consider using cleaner figures. It is unclear how exactly inference on a new dataset is performed. It would improve the clarity of the paper if a specific paragraph on inference is added. Please see specific questions in the questions section.
- The results on the long-term forecasting benchmarks, while reasonable, are not impressive for a "foundational model" that has been trained on a larger corpus of datasets.
- The very-long-term forecasting task is of limited practical significance. Despite that, the discussion requires improvement, e.g., by conducting experiments on more datasets and training the baseline models with the "correct" forecast horizon to put the results in a proper context.
- The zero-shot analysis (Sec. 4.3) has only been conducted on two datasets. Moreover, since prior works such as PatchTST and NHITS do not claim to be foundational models, a proper comparison would be with baselines trained specifically on these held-out datasets. DAM would most likely be worse in that case but it would be a better gauge for the zero-shot performance.
- The very-long-term forecasting task is of limited practical significance. Despite that, the discussion requires improvement, e.g., by conducting experiments on more datasets and training the baseline models with the "correct" forecast horizon to put the results in a proper context.