Summary of Structural Knowledge Informed Continual Multivariate Time Series Forecasting, by Zijie Pan et al.
Structural Knowledge Informed Continual Multivariate Time Series Forecasting
by Zijie Pan, Yushan Jiang, Dongjin Song, Sahil Garg, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka
First submitted to arxiv on: 20 Feb 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 Structural Knowledge Informed Continual Learning (SKI-CL) framework addresses the challenge of modeling variable dependencies in multivariate time series (MTS) forecasting when data is accumulated under different regimes. By leveraging structural knowledge, SKI-CL steers the forecasting model to identify and adapt to these regimes while maintaining performance. The approach combines graph structure learning with consistency regularization to optimize the forecasting objective over MTS data. A representation-matching memory replay scheme maximizes temporal coverage to efficiently preserve underlying dynamics and dependency structures in each regime. Empirical studies demonstrate SKI-CL’s effectiveness for continual MTS forecasting tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of predicting many things that happen together over time is being developed. This approach, called SKI-CL, helps a model learn how different things are connected and adapt to changing conditions. By using special knowledge about the relationships between these things, SKI-CL can make better predictions when new data comes in. The method works by combining two techniques: learning the patterns in the data and ensuring that the model’s understanding of these patterns matches what it already knows. This allows the model to remember many different scenarios and make accurate predictions. |
Keywords
* Artificial intelligence * Continual learning * Regularization * Time series
