Variability in near-Earth solar wind conditions gives rise to space weather, which can have adverse effects on space- and ground-based technologies. Enhanced and sustained solar wind coupling with the Earth's magnetosphere can lead to a geomagnetic storm. The resulting effects can interfere with power transmission grids, potentially affecting today's technology-centered society to great cost. It is therefore important to forecast the intensity and duration of geomagnetic storms to improve decision making capabilities of infrastructure operators. The 150 years aa_H geomagnetic index gives a substantial history of observations from which empirical predictive schemes can be built. Here we investigate the forecasting of geomagnetic activity with two pattern-matching forecast techniques, using the long aa_H record. The techniques we investigate are an Analogue Ensemble (AnEn) Forecast, and a Support Vector Machine (SVM). AnEn produces a probabilistic forecast by explicitly identifying analogs for recent conditions in the historical data. The SVM produces a deterministic forecast through dependencies identified by an interpretable machine learning approach. As a third comparative forecast, we use the 27 days recurrence model, based on the synodic solar rotation period. The methods are analyzed using several forecast metrics and compared. All forecasts outperform climatology on the considered metrics and AnEn and SVM outperform 27 days recurrence. A Cost/Loss analysis reveals the potential economic value is maximized using the AnEn, but the SVM is shown as superior by the true skill score. It is likely that the best method for a user will depend on their need for probabilistic information and tolerance of false alarms.

中文翻译：

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使用模式匹配方法预测地磁活动的发生和强度

近地太阳风条件的变化会引起空间天气，这会对空间和地面技术产生不利影响。增强和持续的太阳风与地球磁层的耦合会导致地磁风暴。由此产生的影响可能会干扰输电网，可能会以巨大的成本影响当今以技术为中心的社会。因此，预测地磁风暴的强度和持续时间以提高基础设施运营商的决策能力非常重要。150 年aa _H地磁指数提供了大量的观测历史，从中可以建立经验预测方案。在这里，我们使用两种模式匹配预测技术研究地磁活动的预测，使用长AA _{^ h}记录。我们研究的技术是模拟集成 (AnEn) 预测和支持向量机 (SVM)。AnEn 通过明确识别历史数据中最近条件的类似物来生成概率预测。SVM 通过可解释的机器学习方法识别的依赖关系生成确定性预测。作为第三个比较预测，我们使用基于会合太阳自转周期的 27 天重复模型。这些方法使用几个预测指标进行分析和比较。在考虑的指标上，所有预测都优于气候学，并且 AnEn 和 SVM 的表现优于 27 天重复。成本/损失分析显示，使用 AnEn 可最大化潜在经济价值，但 SVM 显示为真实技能得分优越。