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本章节探讨了使用scikit-learn和sktime库进行时间序列预测的监督机器学习方法。内容涵盖了时间序列数据预处理、一阶和多阶线性回归模型、非线性模型以及超参数调优。通过实例分析了Air Passengers、Energy Consumption和Daily Temperature数据集，强调了不同时间序列特征对模型性能的影响。同时介绍了Ljung-Box测试、异方差性和Jarque-Bera检验在模型诊断中的应用。
In this chapter, you will explore different machine learning (ML) algorithms for time series forecasting. Machine learning algorithms can be grouped into supervised learning, unsupervised learning, and reinforcement learning. This chapter will focus on supervised machine learning. Preparing time series for supervised machine learning is an important phase that you will be introduced to in the first recipe.
Furthermore, you will explore two machine learning libraries: scikit-Learn and sktime. scikit-learn is a popular machine learning library in Python that offers a wide range of algorithms for supervised and unsupe
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