1. In the intro section, the claim that Transformers reply on regularly sampled time-series data is wrong. For example, [1] shows that the Transformer model handles irregularly-sampled time series well for imputation.
2. In section 2, "Finally, INRs operate on a per-data-instance basis, meaning that one time-series instance is required to train an INR". This claim is true but I don't think it is an advantage. A model that can only handle a single time series data is almost useless.
3. In section 3.1, why $c_i^t$ is a vector rather than a scalar? It just denotes a temporal coordinate of a time and should be a scalar.
4. Why the frequency of the Fourier feature is uniformly sampled?
5. "Vectors Bm ∈ R1×DψF , δm ∈ R1×DψF denote the phase shift and the bias respectively". I think what the author wants to claim is that B represents bias while δm denotes phase shift.
6. In Figure 5, it is unclear what the vertical axis represents.
[1] NRTSI: Non-Recurrent Time Series Imputation, ICASSP 2023.
2. In section 2, "Finally, INRs operate on a per-data-instance basis, meaning that one time-series instance is required to train an INR". This claim is true but I don't think it is an advantage. A model that can only handle a single time series data is almost useless.