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...
decomposition = seasonal_decompose(df['dau'],period=7) #(1) trend = decomposition.trend #(2) cyclical = decomposition.seasonal #(3) noise = decomposition.resid #(4) 1. 2. 3. 4. 语句里,(1)中seasonal_decompose函数的period参数表示周期天数,常取7、30、7*4*3、365,约等于周、月、季、年。
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 ...
show() return trend, seasonal, residual seasonal_decompose(df) 图片 图7 时间序列分解 在看了分解图的四个部分后,可以说,在我们的时间序列中有很强的年度季节性成分,以及随时间推移的增加趋势模式。 时序建模 时间序列数据的适当模型将取决于数据的特定特征,例如,数据集是否具有总体趋势或季节性。请务必选择最...
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验证结果的过程。作为一个例子，金融系列被选择为...
from statsmodels.tsa.seasonal import seasonal_decompose from statsmodels.tsa.stattools import adfuller from statsmodels.graphics.tsaplots import plot_acf #tslearn from tslearn.barycenters import dtw_barycenter_averaging # tssearch from tssearch import get_distance_dict, time_series_segmentation, time_series...
statsmodels.tsa.seasonal的seasonal_decompose()方法需要一个参数freq,它是一个整数值,指定每个季节周期的周期数。由于我们使用的是月度数据,我们期望每个季节年有 12 个周期。该方法返回一个对象,主要包括趋势和季节分量,以及最终的pandas系列数据,其趋势和季节分量已被移除。
decompose数据分解 python代码 数据分解的代码可直接调用python的statsmodels库,如下,就分解成了三部分数据。 fromstatsmodels.tsa.seasonalimportseasonal_decompose decomposition=seasonal_decompose(timeseries)#timeseries时间序列数据trend=decomposition.trend seasonal=decomposition.seasonal ...
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’,...