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

Abstract

The rapid development of time series forecasting research has brought many deep learning-based modules to this field. However, despite the increasing number 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 space 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.