Summary of Time Series Imputation with Multivariate Radial Basis Function Neural Network, by Chanyoung Jung and Yun Jang
Time Series Imputation with Multivariate Radial Basis Function Neural Network
by Chanyoung Jung, Yun Jang
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 researchers aim to address the issue of missing values in time series data by proposing a novel imputation model based on Radial Basis Functions Neural Network (RBFNN). The model learns local information from timestamps to create a continuous function, incorporating time gaps to facilitate learning about missing terms. This model is called Missing Imputation Multivariate RBFNN (MIM-RBFNN), but it has limitations in utilizing temporal information. To address this, they propose an extension, the Missing Value Imputation Recurrent Neural Network with Continuous Function (MIRNN-CF), which uses the continuous function generated by MIM-RBFNN. The performance of both models is evaluated using two real-world datasets with non-random and random missing patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are working on a problem where some data values are missing, but they have no idea what those values should be. They found that one way to solve this problem is by using something called Radial Basis Functions Neural Network (RBFNN), which does a really good job of guessing the missing values. The new model they’re proposing uses RBFNN and adds some extra information from time stamps, so it can do an even better job. They also came up with another idea that combines this model with something called Recurrent Neural Network, to make it even better. They tested both models using real data sets. |
Keywords
» Artificial intelligence » Neural network » Time series
