FTS: A Framework to Find a Faithful TimeSieve

2024-05-30

arXiv.org

AI Summary

TimeSieve models for time-series forecasting suffer from prediction infidelity—exhibiting high sensitivity to random seeds and minor input perturbations. Method: This work formally defines *Faithful TimeSieve* (FTS) and proposes a general, diagnosable, and rectifiable framework integrating sensitivity analysis, robustness optimization, and consistency regularization. It introduces perturbation injection and output stability assessment modules to systematically suppress infidelity. Contribution/Results: The framework significantly reduces seed- and noise-sensitivity across diverse benchmark datasets, simultaneously improving both prediction fidelity and accuracy. It addresses a critical gap in existing time-series forecasting models: the lack of formal stability guarantees.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

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 extbf{underline{F}aithful underline{T}imeunderline{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.

Problem

• Addressing unfaithfulness issues in TimeSieve forecasting model
• Enhancing model stability against seed and noise sensitivity
• Developing framework for robust time series predictions

Innovation

• Defines Faithful TimeSieve (FTS) for reliable predictions
• Proposes framework to fix TimeSieve's unfaithfulness issues
• Enhances model stability and resilience to noise