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

authored by

Difan Deng, Marius Lindauer

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.

Organisation(s)

L3S Research Centre
Machine Learning Section

Type

Conference contribution

Publication date

10.06.2024

Publication status

E-pub ahead of print

Peer reviewed

Yes

Electronic version(s)

https://arxiv.org/abs/2406.05088 (Access: Open)