We consider the problem of predicting the mid-term daily 10.7cm solar radio flux (F10.7), a widely-used solar activity index. A novel approach is proposed for this task, in which BoxCox transformation with a proper parameter is first applied to make the data satisfy the property of homoscedasticity that is a basic assumption of regression models, and then a multi-output linear regression model is used to predict future F10.7 values. The experiment shows that the BoxCox transformation significantly improves the predictive performance and our new approach works substantially better than the prediction from the US Airforce and other alternative methods like Auto-regressiveModel, Multi-layer Perceptron, and Support Vector Regression.

我们考虑了预测中期每日 10.7 厘米太阳射电通量 (F10.7) 的问题，这是一种广泛使用的太阳活动指数。针对该任务提出了一种新方法，其中首先应用具有适当参数的 BoxCox 变换使数据满足作为回归模型基本假设的同方差性，然后使用多输出线性回归模型预测未来的 F10.7 值。实验表明 BoxCox 变换显着提高了预测性能，我们的新方法比美国空军和其他替代方法（如 Auto-regressiveModel、Multi-layer Perceptron 和 Support Vector Regression）的预测效果要好得多。