Although widely explored, time series modeling continues to encounter significant challenges when confronted with real-world data. We propose a novel modeling approach leveraging Implicit Neural Representations (INR). This approach enables us to effectively capture the continuous aspect of time series and provides a natural solution to recurring modeling issues such as handling missing data, dealing with irregular sampling, or unaligned observations from multiple sensors. By introducing conditional modulation of INR parameters and leveraging meta-learning techniques, we address the issue of generalization to both unseen samples and time window shifts. Through extensive experimentation, our model demonstrates state-of-the-art performance in forecasting and imputation tasks, while exhibiting flexibility in handling a wide range of challenging scenarios that competing models cannot.

翻译：尽管时序建模已被广泛探索，但在面对真实世界数据时仍面临重大挑战。本文提出一种利用隐式神经表示（INR）的新型建模方法。该方法能够有效捕捉时序数据的连续性特征，并为缺失数据处理、非均匀采样处理以及多传感器观测未对齐等常见建模难题提供自然解决方案。通过引入INR参数的条件调制机制并融合元学习技术，我们解决了模型对未见样本及时间窗口偏移的泛化问题。大量实验表明，本模型在预测与插补任务中均达到最优性能，同时展现出处理竞争模型无法应对的多种复杂场景的灵活性。