Summary of Point Prediction For Streaming Data, by Aleena Chanda and N. V. Vinodchandran and Bertrand Clarke
Point Prediction for Streaming Data
by Aleena Chanda, N. V. Vinodchandran, Bertrand Clarke
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 proposed methods utilize Count-Min sketch (CMS) and Gaussian process priors with random bias for point prediction in streaming data scenarios. These approaches aim to tackle general predictive problems where no specific model is applicable, commonly referred to as the -open problem class. The CMS-based estimates of distribution functions are shown to be consistent under the assumption that data samples are i.i.d from a fixed distribution function F. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Two new methods for predicting points with streaming data are introduced. One uses Count-Min sketch (CMS) and the other Gaussian process priors with random bias. These approaches work well when there is no true model for the data stream. In statistics, this type of problem is called -open. The paper shows that CMS-based methods can correctly estimate distribution functions if the data comes from a fixed distribution. |
