We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular samples, missing data, or unaligned measurements from multiple sensors. Our method relies on a continuous-time-dependent model of the series' evolution dynamics. It leverages adaptations of conditional, implicit neural representations for sequential data. A modulation mechanism, driven by a meta-learning algorithm, allows adaptation to unseen samples and extrapolation beyond observed time-windows for long-term predictions. The model provides a highly flexible and unified framework for imputation and forecasting tasks across a wide range of challenging scenarios. It achieves state-of-the-art performance on classical benchmarks and outperforms alternative time-continuous models.

翻译：我们提出了一种新颖的时间序列插值与预测建模方法，专门针对现实数据中常见的挑战，如不规则采样、数据缺失或多传感器测量不同步等问题。该方法依赖于序列演化动力学的连续时间依赖模型，并利用适用于序列数据的条件隐式神经表示的适配机制。通过元学习算法驱动的调制机制，该模型能够适应未见样本，并在观测时间窗之外进行长期预测的外推。该模型为具有挑战性的多场景下的插值与预测任务提供了高度灵活且统一的框架，在经典基准测试中达到了最先进的性能，并超越了其他时间连续模型。