Dynamic Time Warping as a means of assessing solar wind time series

During the last decades, continuous 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 adjustments or 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 for space weather purposes. We present the advantages and drawbacks of this method as well as applications on WIND observations and EUHFORIA modeled output at Earth. We show that DTW is a useful tool that permits the evaluation of both fast and slow solar wind. Its distinctive characteristic is that it warps sequences in time, aiming to align them with the minimum cost by using dynamic programming. It can be applied in two dfferent ways for the evaluation of modeled solar wind time series. The first way calculates the so-called sequence similarity factor (SSF), a number that provides a quantification of how good the forecast is, compared to an ideal and a non-ideal (reference) prediction scenarios. The second way quantities 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 (such as, e.g., the correlation coefficient), and point-by-point comparisons. It is a promising technique for the assessment of solar wind profiles offering functions that other metrics do not, so that it can give at once the most complete evaluation portrait of a model.

Publication year:2022

Accessibility:Open