The Space Weather Forecast Testbed (SWFT) is developed by a team of space weather scientists and mathematicians at the University of Southern California (USC) and Jet Propulsion Laboratory (JPL) to foster the creation of models for space weather forecast by exploration of existing historic data using techniques of machine learning. As an example to demonstrate the potential power of SWFT, we present in this paper a multilinear regression‐based forecast model for solar wind. Solar wind is one of the key drivers for numerous physics‐based models for space weather including thermosphere and ionosphere models. Many attempts have been made to produce forecasts for the solar wind. SWFT provides a unified framework for forecast model formulation, training, and performance assessment. In particular, the preparation of training and validation data by SWFT takes into account the realistic constraints on data latency and forecast lead time. In developing a solar wind forecast model, SWFT allows fast exploration of many metaparameters such as the list of predictive variables and their time history used in constructing a model. We present the impact of metaparameter selection, as well as performance relative to existing solar wind forecast models.

中文翻译：

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太阳风的中程预报：带有空间天气预报测试台（SWFT）的建筑回归模型的案例研究

空间天气预报测试台（SWFT）由南加州大学（USC）和喷气推进实验室（JPL）的一组空间天气科学家和数学家开发，旨在通过探索现有历史资料来促进创建空间天气预报模型数据使用机器学习技术。为了证明SWFT的潜力，我们在本文中提出了一种基于多线性回归的太阳风预测模型。太阳风是许多基于物理学的空间天气模型（包括热层和电离层模型）的主要驱动力之一。已经做出许多尝试来产生对太阳风的预测。SWFT为预测模型的制定，培训和绩效评估提供了统一的框架。特别是，SWFT准备培训和验证数据时，考虑了数据延迟和预测交付时间的现实限制。在开发太阳风预报模型时，SWFT允许快速探索许多元参数，例如在构建模型时使用的预测变量列表及其时间历史。我们介绍了元参数选择的影响以及相对于现有太阳风预报模型的性能。