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：我们提出一种新颖的时间序列插补与预测建模方法，旨在应对实际数据中常见的挑战，如不规则采样、数据缺失或多传感器测量不对齐。该方法基于序列演化动力学的连续时间依赖模型，利用条件式隐式神经表征的改进方案处理时序数据。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口外进行外推以实现长期预测。该框架为各类复杂场景下的插补与预测任务提供了高度灵活且统一的解决方案，在经典基准测试中达到最先进性能，并优于其他时间连续模型。