  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.
