Extrinsic regression of time series data consists in predicting the value of a numerical target variable using an input vector which is a time series. The target variable is considered as “extrinsic” as it is not of the same nature as the series values and may not necessarily follow the temporal continuity of the series. This formalization addresses a wide range of problems in different application areas, such as environmental, health or sentiment analysis. In line with the literature on supervised classification of time series, some classification methods have been adapted to the task of regression. Existing regression methods are diverse and use different paradigms, e.g. distance-based methods, interval-based or neural network-based approaches. In parallel to these developments, several libraries for unsupervised feature extraction from time series data have been developed, primarily for descriptive analysis and visualization purposes. In this paper, we combine existing regression methods with signal processing libraries that extract features from time series. To that purpose, the potential of 10 libraries, for the extrinsic regression task, across a set of 61 datasets and six usual regressors is evaluated. The comparative analysis of results from over 3,000 learning ex-periments suggests that unsupervised feature extraction achieves competitive performance for extrinsic regression.