VEST: Automatic Feature Engineering for Forecasting

TL;DR

VEST is an automated feature engineering framework that enhances univariate time series forecasting by summarizing recent dynamics through multiple representations and statistical functions, significantly improving prediction accuracy.

Contribution

We introduce VEST, a novel automated feature engineering method for time series forecasting that combines multiple representations and statistical summaries to improve auto-regressive models.

Findings

Combining VEST features with auto-regression improves forecasting accuracy.

VEST outperforms traditional auto-regressive models on high-frequency time series.

The framework is effective across diverse univariate time series datasets.

Abstract

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.