论文标题
背心：预测的自动功能工程
VEST: Automatic Feature Engineering for Forecasting
论文作者
论文摘要
时间序列预测是一项具有挑战性的任务，在各个域中的应用程序。自动回归是解决这些问题的最常见方法之一。因此，观察结果是通过使用其过去滞后作为预测变量的多重回归来建模的。我们使用统计数据来研究自动回归过程的扩展，这些统计数据概述了最近的时间序列动态。我们研究的结果是一个名为“背心”的新型框架，旨在自动使用单变量和数字时间序列进行功能工程。提出的方法在三个主要步骤中起作用。首先，最近的观察结果映射到不同的表示。其次，每种表示由统计函数总结。最后，将过滤器应用于特征选择。我们发现，将背心与自动回归产生的功能相结合可显着提高预测性能。我们使用具有高采样频率的90个时间序列提供证据。背心在线公开可用。
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. We provide evidence using 90 time series with high sampling frequency. VEST is publicly available online.
