Marginalization Consistent Mixture of Separable Flows for Probabilistic Irregular Time Series Forecasting
<|reference_start|>Marginalization Consistent Mixture of Separable Flows for Probabilistic Irregular Time Series Forecasting: Probabilistic forecasting models for joint distributions of targets in irregular time series are a heavily under-researched area in machine learning with, to the best of our knowledge, only three models researched so far: GPR, the Gaussian Process Regression model~\citep{Durichen2015.Multitask}, TACTiS, the Transformer-Attentional Copulas for Time Series~\cite{Drouin2022.Tactis, ashok2024tactis} and ProFITi \citep{Yalavarthi2024.Probabilistica}, a multivariate normalizing flow model based on invertible attention layers. While ProFITi, thanks to using multivariate normalizing flows, is the more expressive model with better predictive performance, we will show that it suffers from marginalization inconsistency: it does not guarantee that the marginal distributions of a subset of variables in its predictive distributions coincide with the directly predicted distributions of these variables. Also, TACTiS does not provide any guarantees for marginalization consistency. We develop a novel probabilistic irregular time series forecasting model, Marginalization Consistent Mixtures of Separable Flows (moses), that mixes several normalizing flows with (i) Gaussian Processes with full covariance matrix as source distributions and (ii) a separable invertible transformation, aiming to combine the expressivity of normalizing flows with the marginalization consistency of Gaussians. In experiments on four different datasets we show that moses outperforms other state-of-the-art marginalization consistent models, performs on par with ProFITi, but different from ProFITi, guarantee marginalization consistency.<|reference_end|>
@article{yalavarthi2024marginalization,
    title={Marginalization Consistent Mixture of Separable Flows for Probabilistic
  Irregular Time Series Forecasting},
    author={Vijaya Krishna Yalavarthi, Randolf Scholz, Kiran Madhusudhanan, Stefan
  Born, Lars Schmidt-Thieme},
    journal={arXiv preprint arXiv:2406.07246},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07246},
    primaryClass={cs.LG}
}