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本章介绍了如何使用指数平滑法和非季节性、季节性ARIMA模型预测单变量时间序列数据。通过Python实现，包括ACF和PACF图表绘制，检查残差的自相关，模型选择的AIC和BIC等。此外，还探讨了不同统计模型如简单指数平滑、趋势指数平滑、季节性指数平滑及其变种，并提供了不同时间序列数据的案例分析。
In https://blog.youkuaiyun.com/Linli522362242/article/details/127737895, Exploratory Data Analysis and Diagnosis, you were introduced to several concepts to help you understand the time series process. Such recipes included Decomposing time series data, Detecting time series stationarity, Applying power transformations, and Testing for autocorrelation in time series data. These techniques will come in handy in the statistical modeling approach that will be discussed in this chapter.
When working with time series data, different metho
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