  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.
