The field of time series forecasting has garnered significant attention in
recent years, prompting the development of advanced models like TimeSieve,
which demonstrates impressive performance. However, an analysis reveals certain
unfaithfulness issues, including high sensitivity to random seeds and minute
input noise perturbations. Recognizing these challenges, we embark on a quest
to define the concept of \textbf{\underline{F}aithful
\underline{T}ime\underline{S}ieve \underline{(FTS)}}, a model that consistently
delivers reliable and robust predictions. To address these issues, we propose a
novel framework aimed at identifying and rectifying unfaithfulness in
TimeSieve. Our framework is designed to enhance the model's stability and
resilience, ensuring that its outputs are less susceptible to the
aforementioned factors. Experimentation validates the effectiveness of our
proposed framework, demonstrating improved faithfulness in the model's
behavior. Looking forward, we plan to expand our experimental scope to further
validate and optimize our algorithm, ensuring comprehensive faithfulness across
a wide range of scenarios. Ultimately, we aspire to make this framework can be
applied to enhance the faithfulness of not just TimeSieve but also other
state-of-the-art temporal methods, thereby contributing to the reliability and
robustness of temporal modeling as a whole.

我们提出了一个旨在解决 TimeSieve 中不可靠性问题的框架，旨在提高模型的稳定性和可靠性，确保输出对上述因素的影响较小。实验证实了我们提出的框架的有效性，展示了模型行为的改进。我们计划扩大实验范围，进一步验证和优化我们的算法，以确保在各种场景下的全面可靠性，从而增强不仅 TimeSieve 而且其他最先进的时间方法的可靠性和稳健性。