Research Area: Machine Learning
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.
Keywords:
Time series forecasting
Feature engineering
Automatic machine learning
Author(s) Name: Vitor Cerqueira, Nuno Moniz & Carlos Soares
Journal name: Machine Learning
Conferrence name:
Publisher name: Springer
DOI: 10.1007/s10994-021-05959-y
Volume Information:
