Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations

arXiv:2306.05880v5 Announce Type: replace
Abstract: 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 …

abstract arxiv challenges continuous cs.ai cs.lg data forecasting implicit neural representations imputation modeling multiple novel samples sensors series time series type world