用对抗学习提升不规则时间序列预测的准确性与合理性
An Adversarial Learning Approach to Irregular Time-Series Forecasting
- 引入对抗学习框架，平衡全局模式与局部动态建模
- 显著改善预测结果与人类直觉的一致性
- 为不规则时间序列提供新评估思路，适合时序建模研究者
不规则时间序列预测面临两大挑战：模型易受均值回归影响，源于数据噪声大、结构复杂；传统基于误差的评估指标无法捕捉真实模式，且惩罚不切实际的预测，导致结果偏离人类直觉。为此，本文提出一种对抗学习框架，深入分析对抗组件。关键在于平衡全局分布（整体趋势）与过渡动态（局部时序变化）的建模，更精准刻画不规则时间序列的细微特征。本研究为改进模型与评估方法提供实用洞见，并首次将对抗学习应用于不规则时间序列预测领域。
原文摘要 · Abstract (English)
Forecasting irregular time series presents significant challenges due to two key issues: the vulnerability of models to mean regression, driven by the noisy and complex nature of the data, and the limitations of traditional error-based evaluation metrics, which fail to capture meaningful patterns and penalize unrealistic forecasts. These problems result in forecasts that often misalign with human intuition. To tackle these challenges, we propose an adversarial learning framework with a deep analysis of adversarial components. Specifically, we emphasize the importance of balancing the modeling of global distribution (overall patterns) and transition dynamics (localized temporal changes) to better capture the nuances of irregular time series. Overall, this research provides practical insights for improving models and evaluation metrics, and pioneers the application of adversarial learning in the domian of irregular time-series forecasting.
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