  During the last decades, international attempts have been made to develop
realistic space weather prediction tools aiming to forecast the conditions on
the Sun and in the interplanetary environment. These efforts have led to the
development of appropriate metrics in order to assess the performance of those
tools. Metrics are necessary to validate models, compare different models and
monitor improvements of a certain model over time. In this work, we introduce
the Dynamic Time Warping (DTW) as an alternative way to evaluate the
performance of models and, in particular, to quantify differences between
observed and modeled solar wind time series. We present the advantages and
drawbacks of this method as well as applications to WIND observations and
EUHFORIA predictions at Earth. We show that DTW can warp sequences in time,
aiming to align them with the minimum cost by using dynamic programming. It can
be applied in two ways for the evaluation of modeled solar wind time series.
The first, calculates the sequence similarity factor (SSF), a number that
provides a quantification of how good the forecast is, compared to an ideal and
a non-ideal prediction scenarios. The second way quantifies the time and
amplitude differences between the points that are best matched between the two
sequences. As a result, DTW can serve as a hybrid metric between continuous
measurements (e.g., the correlation coefficient), and point-by-point
comparisons. It is a promising technique for the assessment of solar wind
profiles providing at once the most complete evaluation portrait of a model.
