时间序列分析是金融、运维、销售等领域的重要技能。 ## 时间序列基础 ```python import pandas as pd ts_monthly = ts.resample('M').mean() ts_rolling = ts.rolling(window=7).mean() ts_diff = ts.diff() ``` ## 分解 ```python from statsmodels.tsa.seasonal import seasonal_decompose result = seasonal_decompose(ts, model='additive', period=7) ``` ## ARIMA ```python from statsmodels.tsa.arima.model import ARIMA model = ARIMA(ts, order=(1, 1, 1)) fitted = model.fit() forecast = fitted.forecast(steps=30) ``` ## Prophet ```python from prophet import Prophet model = Prophet(yearly_seasonality=True, weekly_seasonality=True) model.fit(df_prophet) forecast = model.predict(future) ``` 时间序列预测的关键：理解数据的趋势、季节性和噪声成分。