Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on the manual choice of an appropriate set of features. Exploiting machine learning methods to extract features from time series automatically becomes crucial in state-of-the-art time series analysis. In this paper, we introduce an automated approach to extract time series features based on time series imaging. We first transform time series into recurrence plots, from which local features can be extracted using computer vision algorithms. The extracted features are used for forecast model averaging. Our experiments show that forecasting based on automatically extracted features, with less human intervention and a more comprehensive view of the raw time series data, yields highly comparable performances with the best methods in the largest forecasting competition dataset (M4) and outperforms the top methods in the Tourism forecasting competition dataset.

基于特征的时间序列表示法已在各种时间序列分析方法中引起了广泛的关注。最近，将时间序列特征用于预测模型平均已成为预测界新兴的研究重点。尽管如此，大多数现有方法都依赖于对一组适当功能的手动选择。利用机器学习方法自动从时间序列中提取特征对于最新的时间序列分析至关重要。在本文中，我们介绍了一种基于时间序列成像的自动方法来提取时间序列特征。我们首先将时间序列转换为递归图，可以使用计算机视觉算法从中提取局部特征。提取的特征用于预测模型平均。