Summary of Optimizing Time Series Forecasting Architectures: a Hierarchical Neural Architecture Search Approach, by Difan Deng and Marius Lindauer
Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach
by Difan Deng, Marius Lindauer
First submitted to arxiv on: 7 Jun 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 paper presents a novel hierarchical neural architecture search approach for time series forecasting tasks, aiming to leverage the full potential of existing deep learning-based modules. The authors design a hierarchical search space that incorporates various architecture types designed for forecasting tasks and enables efficient combination of different forecasting architecture modules. This approach is evaluated on long-term-time-series-forecasting tasks, demonstrating its ability to discover lightweight yet high-performing architectures across different forecasting tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to find the best combinations of deep learning models for predicting time series data. It creates a special search space that allows for mixing and matching different types of forecasting models. This approach is tested on long-term predictions and shows that it can discover simple yet effective models for various forecasting tasks. |
Keywords
» Artificial intelligence » Deep learning » Time series
