Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach
<|reference_start|>Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach: 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.<|reference_end|>
@article{deng2024optimizing,
    title={Optimizing Time Series Forecasting Architectures: A Hierarchical Neural
  Architecture Search Approach},
    author={Difan Deng and Marius Lindauer},
    journal={arXiv preprint arXiv:2406.05088},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05088},
    primaryClass={cs.LG}
}