Time series forecasting is a challenging task with applications in a wide range of domains. Auto-regression is one of the most common approaches to address these problems. Accordingly, observations are modelled by multiple regression using their past lags as predictor variables. We investigate the extension of auto-regressive processes using statistics which summarise the recent past dynamics of time series. The result of our research is a novel framework called VEST, designed to perform feature engineering using univariate and numeric time series automatically. The proposed approach works in three main steps. First, recent observations are mapped onto different representations. Second, each representation is summarised by statistical functions. Finally, a filter is applied for feature selection. We discovered that combining the features generated by VEST with auto-regression significantly improves forecasting performance in a database composed by 90 time series with high sampling frequency. However, we also found that there are no improvements when the framework is applied for multi-step forecasting or in time series with low sample size. VEST is publicly available online.

对于广泛领域的应用而言，时间序列预测是一项具有挑战性的任务。自回归是解决这些问题的最常用方法之一。因此，使用过去的滞后作为预测变量，通过多元回归对观察进行建模。我们使用统计数据调查自回归过程的扩展，这些统计数据总结了时间序列的最新动态。我们研究的结果是一个名为VEST的新颖框架，旨在使用单变量和数字时间序列自动执行要素工程。拟议的方法主要分为三个步骤。首先，将最近的观察结果映射到不同的表示上。其次，每种表示都通过统计功能进行汇总。最后，将过滤器应用于特征选择。我们发现，将VEST生成的功能与自动回归相结合，可以显着提高由90个时间序列和高采样频率组成的数据库的预测性能。但是，我们还发现，将框架应用于多步预测或样本量较小的时间序列时，没有任何改进。VEST可在线公开获得。