In this article, Box-Cox and Yeo-Johnson transformation models are applied to two time series datasets of monthly temperature averages to improve the forecast ability. An application algorithm was proposed to transform the positive original responses using the first model and the stationary responses using the second model to improve the nonparametric estimation of the functional time series. The Box-Cox model contributed to improving the results of the nonparametric estimation of the original data, but the results become somewhat confusing after attempting to make the transformed response variable stationary in the mean, while the functional time series predictions were more accurate using the transformed stationary datasets using the Yeo-Johnson model.

中文翻译：

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使用Yeo-Johnson变换改进功能平稳时间序列的非参数估计及其在温度曲线中的应用

在本文中，将Box-Cox和Yeo-Johnson转换模型应用于两个月平均温度的时间序列数据集，以提高预测能力。提出了一种应用算法，使用第一个模型转换正的原始响应，并使用第二个模型转换平稳的响应，以改善功能时间序列的非参数估计。Box-Cox模型有助于改善原始数据的非参数估计结果，但是在尝试使转换后的响应变量均值保持平稳之后，结果变得有些混乱，而使用转换后的函数时间序列预测则更加准确使用Yeo-Johnson模型的固定数据集。