decomposition = seasonal_decompose(data, model='additive') 对于乘法模型,只需将model参数改为'multiplicative'。 六、可视化分解结果 完成分解后,可以将结果可视化。分解结果包括趋势、季节性和残差部分: trend = decomposition.trend seasonal = decompositio
导入库:首先想要进行seasonal_decompose,要导入必要的库。 加载数据:用pandas加载时间序列数据。 应用分解:使用seasonal_decompose方法进行数据分解。 可视化结果:利用matplotlib查看趋势、季节性和残差。 importpandasaspdimportnumpyasnpfromstatsmodels.tsa.seasonalimportseasonal_decomposeimportmatplotlib.pyplotasplt# 读取数据d...
接下来,我们使用statsmodels库中的seasonal_decompose方法进行季节性因子分解。 from statsmodels.tsa.seasonal import seasonal_decompose # 加法模型分解 decomposition = seasonal_decompose(time_series, model='additive') # 提取分解结果 trend = decomposition.trend seasonal = decomposition.seasonal residual = decomposi...
result_mul = seasonal_decompose(df['value'], model='multiplicative', extrapolate_trend='freq') plt.rcParams.update({'figure.figsize': (10, 10)}) result_mul.plot.suptitle('Multiplicative Decompose') plt.show 加法序列分解 加法序列 = Level + Trend + Seasonality + Error result_add = seasonal_...
import matplotlib.pyplot as plt import datetime from dateutil.relativedelta import relativedelta import seaborn as sns import statsmodels.api as sm from statsmodels.tsa.stattools import acf from statsmodels.tsa.stattools import pacf from statsmodels.tsa.seasonal import seasonal_decompose %matplotlib inline ...
result_add = seasonal_decompose(apple_revenue_history['value']) # 绘图 plt.rcParams.update({'figure.figsize': (32,18)}) 时间序列的平稳性 时间序列与传统的分类和回归预测建模问题不同。时间序列数据是有序的,并且需要平稳性才能进行有意义的摘要统计。
decomposition = seasonal_decompose(self.ts, freq=freq, two_sided=False) # self.ts:时间序列,series类型; # freq:周期,这里为1440分钟,即一天; # two_sided:观察下图2、4行图,左边空了一段,如果设为True,则会出现左右两边都空出来的情况,False保证序列在最后的时间也有数据,方便预测。
fromstatsmodels.tsa.seasonalimportseasonal_decomposefromdateutil.parserimportparse# Import Datadf = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date'], index_col='date')# Multiplicative Decompositionresult_mul = seasonal_decompose(df['value'], ...
fromstatsmodels.tsa.seasonalimportseasonal_decomposefromdateutil.parserimportparse# Import Datadf = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date'], index_col='date')# Multiplicative Decompositionresult_mul = seasonal_decompose(df['value'], ...
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’,...