平稳性和差分
为建模准备序列
平稳性和差分 是 CoddyKit 上的免费 Data Science Academy 课时。 这是第 3 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Data Science Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Data Science Academy 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
Models Like Steady Data
Most forecasting models assume the series behaves consistently over time. That property has a name: stationarity. 🎯
What Stationary Means
A stationary series has a stable mean and spread that do not drift over time. Its statistical mood stays the same.
Trends Break Stationarity
A rising trend means the mean keeps climbing, so the series is non-stationary. Many models will struggle until you fix that.
Seasonality Breaks It Too
Repeating seasonal swings make the average rise and fall on a cycle. That changing pattern also counts as non-stationary.
The ADF Test
You do not have to guess. The ADF test checks stationarity and returns a p-value you can read.
from statsmodels.tsa.stattools import adfuller
result = adfuller(sales)Reading the P-Value
If the ADF p-value is below 0.05, the series is likely stationary. Above it, you probably still have a trend to remove.
Differencing to the Rescue
Differencing replaces each value with how much it changed from the last step. This simple move often erases a trend.
diffed = sales.diff()Why It Removes Trends
By looking at changes instead of levels, a steady climb becomes a flat line wobbling around zero, which is stationary.
Drop the First NaN
The very first row has nothing to subtract, so diff leaves it NaN. Drop it before testing or modeling.
diffed = sales.diff().dropna()Differencing Again
If one round is not enough, difference the result a second time. The number of rounds becomes the d term in ARIMA.
Seasonal Differencing
To kill a yearly cycle, subtract the value 12 steps back. That seasonal diff targets the repeating pattern directly.
seasonal = sales.diff(12)Quick Check
An ADF test on your raw series returns a p-value of 0.40. What does that suggest?
Recap: Make It Steady
You learned to test stationarity with ADF and to remove trends and cycles using differencing before modeling.
常见问题解答
「平稳性和差分」课时是免费的吗？
是的 — 「平稳性和差分」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Data Science Academy 课程的其余内容，请升级到 CoddyKit PRO。 Data Science Academy 课程共包含 4 节课。
「平稳性和差分」这节课中我会学到什么？
为建模准备序列 你通过在浏览器中直接运行的动手代码来练习 Data Science Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Data Science Academy 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Data Science Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 3 节课，共 4 节。
「平稳性和差分」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Data Science Academy 课中编写并运行代码吗？
能。每节 Data Science Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。