吴同学2025-02-25 18:48:14
吴同学ACF与PACF如何理解他们之间的区别
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黄石2025-02-26 09:42:20
黄石同学你好。自相关系数描述 Yt 在任意两个不同时刻 t，t − h 的取值之间的相关程度。然而，该指标直接反映了 Yt 与 Yt−h 之间的相关关系，而没有控制中间 h − 1 个随机变量的变动。偏自相关系数则在将中间 h − 1 个随机变量看作已知的条件下研究 Yt 与 Yt−h 之间的相关关系，可通过 h 阶自回归拟合得到。见下图。
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根据AR和MA的公式，怎么理解与之相应的ACF,PACF的图像
180****86962025-02-27 11:06:47
ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) are both tools used to analyze the relationships between time series data at different lags, but they measure different aspects. ACF shows the correlation between the time series and its own lagged values at various lags, capturing the overall dependency structure, including both direct and indirect relationships. It is useful for understanding the general pattern of autocorrelation in the data. PACF, on the other hand, measures the correlation between the time series and its lagged values after removing the effects of shorter lags. Essentially, it isolates the direct relationship between the series and its lags, without the influence of intermediate lags. The main difference between them is that ACF captures both direct and indirect dependencies, while PACF isolates only the direct dependency between a time series and its lagged values. PACF is often used to determine the order of AR models while ACF for MA.
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