4. 使用seasonal_decompose函数进行分解 decomposition = seasonal_decompose(data, model='multiplicative') 5. 可视化分解结果 trend = decomposition.trend seasonal = decomposition.seasonal residual = decomposition.resid plt.figur
分解后的结果seasonal_decompose 库用户分解后的结果seasonal_decompose 库用户seasonal_decompose(data, model)返回趋势、季节性、残差 在实现上,seasonal_decompose函数的核心代码段可能如下所示: fromstatsmodels.tsa.seasonalimportseasonal_decompose result=seasonal_decompose(data,model='additive')trend=result.trend seas...
parse_dates=['date'],index_col='date')# 将索引转换为 DateTimeIndexdata.index=pd.to_datetime(data.index)# 确定时间频率data=data.asfreq('M')# 应用季节性分解result=seasonal_decompose(data['sales'],model='additive')result.plot()
问python将seasonal_decompose应用于金融时间序列EN本文的目的是展示使用时间序列从数据处理到构建神经网络和...
在Python中,我们可以使用statsmodels库中的seasonal_decompose函数来实现泰尔指数分解。 我们需要导入所需的库和模块: ``` import pandas as pd from statsmodels.tsa.seasonal import seasonal_decompose ``` 接下来,我们需要准备要进行分解的时间序列数据。假设我们有一个名为data的DataFrame,其中包含了时间序列数据,...
函数解析 statsmodels.tsa.seasonal.seasonal_decompose( x,model = ‘additive‘, filt = None,period = None,two_side = True, extrapolate_trend = 0) 参数: x:array_like, 被分解的数据。model:{“additive”, “multiplicative”}, optional "additive"(加法模型)和"multiplicative"(乘法模型) ...
seasonal_decompose 函数返回一个带有季节性、趋势和残差属性的对象,我们可以从系列值中减去它们。 from statsmodels.tsa.seasonal import seasonal_decompose from dateutil.parser import parse df[0].plot(figsize=(32,18)) df[0] = df[0] - decompose.trend ...
同时可以使用seasonal_decompose函数进行分析,可以看出季节性非常明显 decomposition = seasonal_decompose(df.riders, freq=12) fig = plt.figure() fig = decomposition.plot() fig.set_size_inches(15, 8) #可以分别获得趋势、季节性和随机性 trend = decomposition.trend seasonal = decomposition.seasonal residual...
我们将使用python的statmodels函数seasonal_decomposition。 result=seasonal_decompose(df['#Passengers'], model='multiplicable', period=12) 在季节性分解中,我们必须设置模型。我们可以将模型设为加的或乘的。选择正确模型的经验法则是,在我们的图中查看趋势和季节性变化是否在一段时间内相对恒定,换句话说,是线性...
import numpy as npfrompandas import read_csvimport matplotlib.pyplot as pltfrom statsmodels.tsa.seasonal import seasonal_decomposefrom pylab import rcParams elecequip = read_csv(r“C:/Users/datas/python/data/elecequip.csv”)result = seasonal_decompose(np.array(elecequip), model=‘multiplicative’,...