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

TL;DR

AutoTS is an automated time series forecasting model design method that uses a two-stage pruning strategy and a knowledge graph to efficiently generate high-quality forecasting models tailored to specific data scenarios.

Contribution

The paper introduces AutoTS, a novel approach combining design experience, a two-stage pruning strategy, and a knowledge graph to automate and improve TSF model design.

Findings

AutoTS outperforms existing neural architecture search algorithms in efficiency.

AutoTS generates more powerful TSF models than manual designs.

AutoTS effectively supports diverse TSF tasks with a large search space.

Abstract

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.