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arXiv 2103.07542astro-ph.SRcs.LG
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如何训练你的耀斑预测模型:回顾罕见事件的鲁棒抽样

How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events

Georgia State University(佐治亚州立大学);Academy of Athens(雅典学院);University of Hawai‘i at Mānoa(夏威夷大学马诺阿分校)

Azim Ahmadzadeh, Berkay Aydin, Manolis K. Georgoulis, Dustin J. Kempton, Sushant S. Mahajan, Rafal A. Angryk

摘要:

我们提出了一个案例研究的太阳耀斑预测的元数据特征时间序列,通过处理它作为一个突出的类不平衡和时间相干的问题。通过太阳耀斑(SWAN-SF)基准数据集的空间天气分析,充分利用太阳活动区的耀斑前时间序列成为可能;活动区属性的多变量时间序列的分区集合,包括4075个区域,跨越9年的太阳动力学观测站(SDO)运行期间。我们展示了时间序列预测中连续性需求引发的时间相干性的一般概念,并表明缺乏对这种效应的正确理解可能会虚假地提高模型的性能。我们进一步解决了罕见事件预测中另一个众所周知的挑战,即类不平衡问题。SWAN-SF是一个合适的数据集,GOES M级和X级耀斑的不平衡比为60:1,X级耀斑与耀斑安静实例的不平衡比为800:1。我们重新审视了这些挑战的主要补救措施,并提出了几个实验来说明这些补救措施中的每一个可能对性能产生的确切影响。此外,我们承认一些基本的数据操作任务,如数据规范化和交叉验证也可能影响性能-我们也讨论了这些问题。在这个框架中,我们还审查了使用真正的技能统计和Heidke技能得分,作为两个广泛使用的性能验证指标的耀斑预测任务的主要优点和缺点。总之,我们展示并倡导时间序列与时间点预测的优势,前提是上述挑战得到可衡量和定量的解决。

英文摘要:

We present a case study of solar flare forecasting by means of metadata feature time series, by treating it as a prominent class-imbalance and temporally coherent problem. Taking full advantage of pre-flare time series in solar active regions is made possible via the Space Weather Analytics for Solar Flares (SWAN-SF) benchmark dataset; a partitioned collection of multivariate time series of active region properties comprising 4075 regions and spanning over 9 years of the Solar Dynamics Observatory (SDO) period of operations. We showcase the general concept of temporal coherence triggered by the demand of continuity in time series forecasting and show that lack of proper understanding of this effect may spuriously enhance models' performance. We further address another well-known challenge in rare event prediction, namely, the class-imbalance issue. The SWAN-SF is an appropriate dataset for this, with a 60:1 imbalance ratio for GOES M- and X-class flares and a 800:1 for X-class flares against flare-quiet instances. We revisit the main remedies for these challenges and present several experiments to illustrate the exact impact that each of these remedies may have on performance. Moreover, we acknowledge that some basic data manipulation tasks such as data normalization and cross validation may also impact the performance -- we discuss these problems as well. In this framework we also review the primary advantages and disadvantages of using true skill statistic and Heidke skill score, as two widely used performance verification metrics for the flare forecasting task. In conclusion, we show and advocate for the benefits of time series vs. point-in-time forecasting, provided that the above challenges are measurably and quantitatively addressed.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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