Summary of Aalf: Almost Always Linear Forecasting, by Matthias Jakobs et al.
AALF: Almost Always Linear Forecasting
by Matthias Jakobs, Thomas Liebig
First submitted to arxiv on: 16 Sep 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 A novel online model selection framework is proposed to address the issue of lack of understanding in the underlying decision process of Deep Learning (DL) models for time-series forecasting. The framework learns to identify important predictions that require DL’s high predictive power, while using simple, interpretable methods like ARIMA for the rest. An empirical study on real-world datasets shows comparable performance to state-of-the-art online model selection methods while being more interpretable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting uses Deep Learning models to make accurate predictions. However, these models are hard to understand and might not be needed all the time. A new way to choose between simple and complex models is proposed. It learns which predictions need the complex models and which can use simpler ones. This makes the process more understandable. |
Keywords
» Artificial intelligence » Deep learning » Time series
