Summary of Imputation with Inter-series Information From Prototypes For Irregular Sampled Time Series, by Zhihao Yu et al.
Imputation with Inter-Series Information from Prototypes for Irregular Sampled Time Series
by Zhihao Yu, Xu Chu, Liantao Ma, Yasha Wang, Wenwu Zhu
First submitted to arxiv on: 14 Jan 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 This paper proposes a novel method called PRIME (Prototype Recurrent Imputation ModEl) for imputing missing values in irregularly sampled time series. By leveraging both intra-series and inter-series information, PRIME aims to reduce uncertainty and memorization effect in existing methods. The framework consists of three modules: a prototype memory module for learning inter-series information, a bidirectional gated recurrent unit utilizing prototype information for imputation, and an attentive prototypical refinement module for adjusting imputations. Experimental results on three datasets show that PRIME outperforms state-of-the-art models by up to 26% relative improvement in mean square error. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to fill in missing data points in time series that aren’t taken at regular intervals. This can be hard because we don’t have enough information from the same time period. The researchers created a new method called PRIME that uses both what’s happening within one set of data and between different sets to make more accurate predictions. They tested their method on three different datasets and found it was up to 26% better than other methods at getting it right. |
Keywords
* Artificial intelligence * Time series
