Summary of Marginalization Consistent Mixture Of Separable Flows For Probabilistic Irregular Time Series Forecasting, by Vijaya Krishna Yalavarthi et al.
Marginalization Consistent Mixture of Separable Flows for Probabilistic Irregular Time Series Forecasting
by Vijaya Krishna Yalavarthi, Randolf Scholz, Kiran Madhusudhanan, Stefan Born, Lars Schmidt-Thieme
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel probabilistic forecasting model for irregular time series called Marginalization Consistent Mixtures of Separable Flows (moses). The model addresses the limitations of existing models like GPR, TACTiS, and ProFITi in achieving marginalization consistency. Moses combines Gaussian Processes with full covariance matrix as source distributions and a separable invertible transformation to ensure both expressivity and consistency. Experimental results on four datasets show that moses outperforms other state-of-the-art models while guaranteeing marginalization consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new model called Marginalization Consistent Mixtures of Separable Flows (moses) for forecasting irregular time series. It fixes the problems with existing models like ProFITi and TACTiS by making sure that the predicted distributions match the individual variable distributions. The new model does this by using a combination of Gaussian Processes and special transformations. This makes it better at predicting the future than other models while also being more accurate in its predictions. |
Keywords
» Artificial intelligence » Time series
