Probabilistic Learning of Multivariate Time Series with Temporal Irregularity

• URL: http://arxiv.org/abs/2306.09147v3
• Date: Sat, 15 Feb 2025 10:15:52 GMT
• Title: Probabilistic Learning of Multivariate Time Series with Temporal Irregularity
• Authors: Yijun Li, Cheuk Hang Leung, Qi Wu,
• Abstract summary: Real-world time series often suffer from temporal irregularities, including nonuniform intervals and misaligned variables. We propose an end-to-end framework that models temporal irregularities while capturing the joint distribution of variables at arbitrary continuous-time points.
• Score: 21.361823581838355
• License:
• Abstract: Probabilistic forecasting of multivariate time series is essential for various downstream tasks. Most existing approaches rely on the sequences being uniformly spaced and aligned across all variables. However, real-world multivariate time series often suffer from temporal irregularities, including nonuniform intervals and misaligned variables, which pose significant challenges for accurate forecasting. To address these challenges, we propose an end-to-end framework that models temporal irregularities while capturing the joint distribution of variables at arbitrary continuous-time points. Specifically, we introduce a dynamic conditional continuous normalizing flow to model data distributions in a non-parametric manner, accommodating the complex, non-Gaussian characteristics commonly found in real-world datasets. Then, by leveraging a carefully factorized log-likelihood objective, our approach captures both temporal and cross-sectional dependencies efficiently. Extensive experiments on a range of real-world datasets demonstrate the superiority and adaptability of our method compared to existing approaches.

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