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Automatic Time Series Forecasting (TSF) model design which aims to help usersto efficiently design suitable forecasting model for the given time series datascenarios, is a novel research topic to be urgently solved. In this paper, wepropose AutoTS algorithm trying to utilize the existing design skills anddesign efficient search methods to effectively solve this problem. In AutoTS,we extract effective design experience from the existing TSF works. We allowthe effective combination of design experience from different sources, so as tocreate an effective search space containing a variety of TSF models to supportdifferent TSF tasks. Considering the huge search space, in AutoTS, we propose atwo-stage pruning strategy to reduce the search difficulty and improve thesearch efficiency. In addition, in AutoTS, we introduce the knowledge graph toreveal associations between module options. We make full use of theserelational information to learn higher-level features of each module option, soas to further improve the search quality. Extensive experimental results showthat AutoTS is well-suited for the TSF area. It is more efficient than theexisting neural architecture search algorithms, and can quickly design powerfulTSF model better than the manually designed ones.

AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning