Thanks for your feedback. We provide a point-by-point response to your new concerns below.
**1. ....however, the improvement in predictive performance is neither significant nor consistent across all datasets, particularly on the Traffic dataset.**
A1. We think your comment is not professional about improvements in the domain of time series forecasting. We don't understand what level of improvement is considered sufficient in your opinion.
As you mentioned, TimeXer, what you refer to as a more recent and powerful method,
its performance is worse than iTransformer on the ECL, Traffic, and Weather datasets, and worse than RLinear on ETTh2 dataset (please see its Table 8). Similarly, iTransformer shows weaker performance on the ETTh and ETTm datasets. Are you also questioning their performance improvements, too? It is **ridiculous** the failure to achieve optimal performance across all datasets could also be a reason of rejection.
**2. How many experimental runs were conducted for the reported results? From my understanding, the results of deep learning algorithms can vary across different runs. Can you guarantee that your improvements are statistically significant across all datasets? Additionally, what are the standard deviations across all runs?**
A2. Your question seems to be not related to our submission.
We are wondering if you generate this review comments by Generative AI (e.g., ChatGPT).
If you have run the state-of-the-art baselines in the domain of time series forecasting (e.g., iTransformer, PatchTST),
you would know these forecasting methods are usually stable thus they did not report std.
We conducted the experiments three times with three seeds, and reported the average performance with the very stable results and less std.
Thus we followed previous settings to report the performance.
The average improvement of FilterNet over all baseline models is statistically significant at the confidence of 95% (**please see in line 218 of our paper**).
**3. The performance of TexFilter and PaiFilter varies significantly across different datasets. What is the underlying reason for this fluctuation? It’s difficult to determine when to use TexFilter versus PaiFilter. A more rigorous justification or empirical study is needed. Otherwise, I would prefer a model that demonstrates consistent performance across all scenarios, such as PatchTST and [1, 2].**
A3. In the literature, compared with other model architectures, MLP-based and Transformer-based methods have achieved competitive performance. Inspired by the two paradigms, we design two corresponding types of filters. i.e., PaiFilter and TexFilter. PaiFilter offer stability and efficiency, making them suitable for tasks with static relationships. In contrast, TexFilter provides the flexibility to capture dynamic dependencies, excelling in tasks that require context-sensitive analysis. As shown in Table 1, PaiFilter performs well on small datasets (e.g., ETTh1), while TexFilter excels on large datasets (e.g., Traffic) due to the ability to model the more complex and contextual correlations present in larger datasets. This dual approach allows FilterNet to effectively handle a wide range of data types and forecasting scenarios, combining the best aspects of both paradigms to achieve superior performance.
**We have analyzed the two filters in Appendix B and have also given explanations about the results of Table 1 in lines 219-224**.
You again mentioned a **ridiculous** point. We don't know why the proposed method should be one model. Like DLinear, it also has studied three different variants, i.e., NLinear, Linear, and DLinear. Can you say you do not want to see the Linear family models in one paper?
**4. ... comparing FITS and iTransformer, more recent and powerful baselines, such as TimeXer [1] and Timer [2], should also be considered.**
A4.
We have also compared FilterNet with SparseTSF (ICML, 2024) and Koopa (NeurIPS, 2023), and we are very confused that why we should compare with TimeXer which as only one preprint paper, and Timer as a generative pre-trained large model.
**What is even more crazy** is that you recommend we include Timer in the efficiency comparison. Timer is a pre-trained model, which has a 28B pre-trained parameters (see its Table 9). It is brutal.
**5. Why didn't the paper compare the efficiency of frequency-based methods (e.g., FITS, FilM) with the proposed method? Compared to other types of models, it is more necessary to compare it with them.Moreover, it would be beneficial to include TimeXer and Timer in the efficiency comparison as well.**
A5. We have compared the efficiency with FiLM, please refer to A6 in Response to Reviewer 2waa.
For FITS, it is a lightweight method and has 10k parameters. Obviously, FITS is more efficient. However, our model is not designed for a lightweight model, but a model that balances efficiency and effectiveness.
As for the efficiency comparison with TimeXer and Timer, please refer A4.