Before revision:  On a selection of artificial and real datasets  we demonstrate the performance  of DCCA under a variety of conditions  .
After revision:  On a selection of artificial and real datasets we demonstrate the performance of DCCA under a variety of conditions . We compare the DCCA algorithm to other warping based methods, DCCA outperforms dynamic time warping and cross correlation based methods by an order of magnitude in terms of alignment error. DCCA performs especially well on almost periodic biomedical signals such as heart-beats and breathing patterns. In comparison to existing approaches, that are not tailored towards raw sensor data, DCCA is not only fast enough to work on signals with billions of data points but also provides automatic filtering and transformation functionalities, allowing it to deal with very noisy and even morphologically distinct signals .