Summary of Joint Prediction Regions For Time-series Models, by Eshant English
Joint Prediction Regions for time-series models
by Eshant English
First submitted to arxiv on: 14 May 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 This paper tackles the issue of providing prediction intervals for machine learning models, which is crucial in many applications where confidence in predictions is essential. The authors propose a method based on bootstrapping to construct joint prediction regions (JPRs) for time series data, which is challenging due to dependencies between observations. The method is compared with other approaches like NP heuristic and Joint Marginals using different datasets and predictors (e.g., ARIMA and LSTM). A novel technique is also developed to estimate prediction standard errors for various models. Experimental results show that the method can effectively control the width of JPRs, with strong predictors like neural nets producing narrower intervals, while longer forecast horizons and lower significance levels lead to wider intervals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions by giving us a way to be more confident in our answers. Machine learning models are great at making single predictions, but they often don’t tell us how likely those predictions are. This is important because sometimes we need to know not just what will happen, but also how likely it is. The authors came up with a new method that can help with this using something called bootstrapping. They tested their method on different types of data and models, including neural networks, to see if it works well. The results show that their method can be useful in certain situations. |
Keywords
» Artificial intelligence » Bootstrapping » Lstm » Machine learning » Time series
