Automated Feature Engineering for Time Series Data

Most machine learning algorithms today are not time-aware and are not easily applied to time series and forecasting problems. Leveraging advanced algorithms like XGBoost, or even linear models, typically requires substantial data preparation and feature engineering – for example, creating lagged features, detrending the target, and detecting periodicity. The preprocessing required becomes more difficult in the common case where the problem requires predicting a window of multiple future time points. As a result, most practitioners fall back on classical methods, such as ARIMA or trend analysis, which are time-aware but less expressive. This article covers the best practices for solving this challenge, by introducing a general framework for developing time series models, generating features and preprocessing the data, and exploring the potential to automate this process in order to apply advanced machine learning algorithms to almost any time series problem.