Probabilistic Learning of Multivariate Time Series with Temporal
Irregularity
- URL: http://arxiv.org/abs/2306.09147v2
- Date: Fri, 16 Jun 2023 05:56:20 GMT
- Title: Probabilistic Learning of Multivariate Time Series with Temporal
Irregularity
- Authors: Yijun Li, Cheuk Hang Leung, Qi Wu
- Abstract summary: temporal irregularities, including nonuniform time intervals and component misalignment.
We develop a conditional flow representation to non-parametrically represent the data distribution, which is typically non-Gaussian.
The broad applicability and superiority of the proposed solution are confirmed by comparing it with existing approaches through ablation studies and testing on real-world datasets.
- Score: 25.91078012394032
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multivariate sequential data collected in practice often exhibit temporal
irregularities, including nonuniform time intervals and component misalignment.
However, if uneven spacing and asynchrony are endogenous characteristics of the
data rather than a result of insufficient observation, the information content
of these irregularities plays a defining role in characterizing the
multivariate dependence structure. Existing approaches for probabilistic
forecasting either overlook the resulting statistical heterogeneities, are
susceptible to imputation biases, or impose parametric assumptions on the data
distribution. This paper proposes an end-to-end solution that overcomes these
limitations by allowing the observation arrival times to play the central role
of model construction, which is at the core of temporal irregularities. To
acknowledge temporal irregularities, we first enable unique hidden states for
components so that the arrival times can dictate when, how, and which hidden
states to update. We then develop a conditional flow representation to
non-parametrically represent the data distribution, which is typically
non-Gaussian, and supervise this representation by carefully factorizing the
log-likelihood objective to select conditional information that facilitates
capturing time variation and path dependency. The broad applicability and
superiority of the proposed solution are confirmed by comparing it with
existing approaches through ablation studies and testing on real-world
datasets.
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