The Dynamic Time Warping as a means to assess solar wind time series. (arXiv:2109.07873v1 [astro-ph.SR])

The Dynamic Time Warping as a means to assess solar wind time series. (arXiv:2109.07873v1 [astro-ph.SR])
<a href="http://arxiv.org/find/astro-ph/1/au:+Samara_E/0/1/0/all/0/1">Evangelia Samara</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Laperre_B/0/1/0/all/0/1">Brecht Laperre</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kieokaew_R/0/1/0/all/0/1">Rungployphan Kieokaew</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Temmer_M/0/1/0/all/0/1">Manuela Temmer</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Verbeke_C/0/1/0/all/0/1">Christine Verbeke</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Rodriguez_L/0/1/0/all/0/1">Luciano Rodriguez</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Magdalenic_J/0/1/0/all/0/1">Jasmina Magdalenic</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Poedts_S/0/1/0/all/0/1">Stefaan Poedts</a>

During the last decades there is a continuing international endeavor in
developing realistic space weather prediction tools aiming to forecast the
conditions on the Sun and in the interplanetary environment. These efforts have
led to the need of developing appropriate metrics in order to assess the
performance of those tools. Metrics are necessary for validating models,
comparing different models and monitoring adjustments or improvements of a
certain model over time. In this work, we introduce the Dynamic Time Warping
(DTW) as an alternative way to validate models and, in particular, to quantify
differences between observed and synthetic (modeled) 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 L1. We
show that DTW is a useful tool that permits the evaluation of both the 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 different 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 a best and a worst case 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, it can serve as a hybrid metric
between continuous measurements (such as, e.g., the correlation coefficient)
and point-by-point comparisons. We conclude that DTW 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 profile of a
model.

During the last decades there is a continuing international endeavor in
developing realistic space weather prediction tools aiming to forecast the
conditions on the Sun and in the interplanetary environment. These efforts have
led to the need of developing appropriate metrics in order to assess the
performance of those tools. Metrics are necessary for validating models,
comparing different models and monitoring adjustments or improvements of a
certain model over time. In this work, we introduce the Dynamic Time Warping
(DTW) as an alternative way to validate models and, in particular, to quantify
differences between observed and synthetic (modeled) 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 L1. We
show that DTW is a useful tool that permits the evaluation of both the 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 different 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 a best and a worst case 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, it can serve as a hybrid metric
between continuous measurements (such as, e.g., the correlation coefficient)
and point-by-point comparisons. We conclude that DTW 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 profile of a
model.

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