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arXiv · 2406.05088

Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach

Difan Deng·Marius Lindauer
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Abstract

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.

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Difan Deng, Marius Lindauer. 2024-06-07. Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. https://arxiv.org/abs/2406.05088

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