AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning
This work addresses the need for efficient model design in time series forecasting, offering a practical solution for users in this domain, though it appears incremental as it builds on existing design skills and search methods.
The paper tackles the problem of automatically designing time series forecasting models by proposing AutoTS, which uses a two-stage pruning strategy and knowledge graphs to efficiently search a large design space, resulting in models that outperform manually designed ones and are more efficient than existing neural architecture search algorithms.
Automatic Time Series Forecasting (TSF) model design which aims to help users to efficiently design suitable forecasting model for the given time series data scenarios, is a novel research topic to be urgently solved. In this paper, we propose AutoTS algorithm trying to utilize the existing design skills and design efficient search methods to effectively solve this problem. In AutoTS, we extract effective design experience from the existing TSF works. We allow the effective combination of design experience from different sources, so as to create an effective search space containing a variety of TSF models to support different TSF tasks. Considering the huge search space, in AutoTS, we propose a two-stage pruning strategy to reduce the search difficulty and improve the search efficiency. In addition, in AutoTS, we introduce the knowledge graph to reveal associations between module options. We make full use of these relational information to learn higher-level features of each module option, so as to further improve the search quality. Extensive experimental results show that AutoTS is well-suited for the TSF area. It is more efficient than the existing neural architecture search algorithms, and can quickly design powerful TSF model better than the manually designed ones.