Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912665982468096 |
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| author | Deng, Difan Lindauer, Marius |
| author_facet | Deng, Difan Lindauer, Marius |
| contents | The rapid development of time series forecasting research has brought many deep learning-based modules in this field. However, despite the increasing amount of new forecasting architectures, it is still unclear if we have leveraged the full potential of these existing modules within a properly designed architecture. In this work, we propose a novel hierarchical neural architecture search approach for time series forecasting tasks. With the design of a hierarchical search space, we incorporate many architecture types designed for forecasting tasks and allow for the efficient combination of different forecasting architecture modules. Results on long-term-time-series-forecasting tasks show that our approach can search for lightweight high-performing forecasting architectures across different forecasting tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05088 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach Deng, Difan Lindauer, Marius Machine Learning The rapid development of time series forecasting research has brought many deep learning-based modules in this field. However, despite the increasing amount of new forecasting architectures, it is still unclear if we have leveraged the full potential of these existing modules within a properly designed architecture. In this work, we propose a novel hierarchical neural architecture search approach for time series forecasting tasks. With the design of a hierarchical search space, we incorporate many architecture types designed for forecasting tasks and allow for the efficient combination of different forecasting architecture modules. Results on long-term-time-series-forecasting tasks show that our approach can search for lightweight high-performing forecasting architectures across different forecasting tasks. |
| title | Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.05088 |