FTS: A Framework to Find a Faithful TimeSieve
<|reference_start|>FTS: A Framework to Find a Faithful TimeSieve: 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, input and layer noise perturbations and parametric 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 faithfulness, 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.<|reference_end|>
@article{lai2024fts:,
    title={FTS: A Framework to Find a Faithful TimeSieve},
    author={Songning Lai, Ninghui Feng, Jiechao Gao, Hao Wang, Haochen Sui, Xin
  Zou, Jiayu Yang, Wenshuo Chen, Hang Zhao, Xuming Hu, Yutao Yue},
    journal={IJCAI2024 workshop},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19647},
    primaryClass={cs.LG}
}