Summary of Task-oriented Time Series Imputation Evaluation Via Generalized Representers, by Zhixian Wang and Linxiao Yang and Liang Sun and Qingsong Wen and Yi Wang
Task-oriented Time Series Imputation Evaluation via Generalized Representers
by Zhixian Wang, Linxiao Yang, Liang Sun, Qingsong Wen, Yi Wang
First submitted to arxiv on: 9 Oct 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 proposed approach combines time series imputation with neural network models used for downstream tasks. It evaluates the performance of different imputation strategies on forecasting, anomaly detection, classification, and other tasks without retraining the models. The approach estimates the gain of each imputation strategy and recommends the most favorable one based on its estimated gain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us predict what will happen in the future (forecasting), find unusual patterns (anomaly detection), or group things into categories (classification). When we don’t have all the data, this can cause problems. To fix this, researchers usually focus on making the missing values look like they belong with the rest of the data. But, this doesn’t consider what will happen when we use this new data for other tasks. This paper is trying to solve this problem by looking at how well different ways of filling in the missing values work for specific tasks. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Neural network » Time series
