Summary of Is Precise Recovery Necessary? a Task-oriented Imputation Approach For Time Series Forecasting on Variable Subset, by Qi Hao et al.
Is Precise Recovery Necessary? A Task-Oriented Imputation Approach for Time Series Forecasting on Variable Subset
by Qi Hao, Runchang Liang, Yue Gao, Hao Dong, Wei Fan, Lu Jiang, Pengyang Wang
First submitted to arxiv on: 15 Nov 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 The proposed Task-Oriented Imputation for Variable Subset Forecasting (TOI-VSF) framework tackles a unique challenge in multivariate time series forecasting where available variables are only a subset of those used during training. Unlike traditional imputation methods, TOI-VSF prioritizes supporting the downstream forecasting task by incorporating a self-supervised imputation module that preserves temporal patterns and vital characteristics. A joint learning strategy ensures the imputation process aligns with and benefits the forecasting objective. Evaluation on four datasets shows an average improvement of 15% over baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Variable Subset Forecasting (VSF) is a type of time series forecasting where some variables are missing. The paper proposes a new way to deal with this problem called Task-Oriented Imputation for VSF (TOI-VSF). Instead of trying to fill in all the missing data, TOI-VSF focuses on making sure that the missing data doesn’t affect how well the forecast works. The authors test their idea on four different datasets and show that it works better than other methods. |
Keywords
» Artificial intelligence » Self supervised » Time series
