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...
importpandasaspdimportnumpyasnpfromstatsmodels.tsa.seasonalimportseasonal_decomposeimportmatplotlib.pyplotasplt# 读取数据data=pd.read_csv('time_series_data.csv')data['date']=pd.to_datetime(data['date'])data.set_index('date',inplace=True)# 分解decomposition=seasonal_decompose(data['value'],model='...
trend = decomposition.trend seasonal = decomposition.seasonal residual = decomposition.resid 举例说明 生成数据 import numpy as np import pandas as pd from statsmodels.tsa.seasonal import seasonal_decompose import matplotlib.pyplot as plt df = pd.DataFrame(np.random.randint(1, 10, size=(365, 1)),...
import pandas as pd import numpy as np from statsmodels.tsa.seasonal import seasonal_decompose #https://www.kaggle.com/rakannimer/air-passengers df=pd.read_csv(‘AirPassengers.csv’) df.head() 首先,我们需要将Month列设置为索引,并将其转换为Datetime对象。 df.set_index('Month',inplace=True) ...
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 ...
使用StatsModels 进行分解 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 ...
statsmodels包里的seasonal_decompose使用起来非常方便。 from statsmodels.tsa.seasonal import seasonal_decomposefrom dateutil.parser import parse # Import Datadf = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv',...
from statsmodels.tsa.seasonal import seasonal_decompose decomposition = seasonal_decompose(ts, model="additive",period=6) trend = decomposition.trend #趋势序列 seasonal = decomposition.seasonal #季节序列 residual = decomposition.resid #随机序列 白噪声检验 对于平稳序列,只有那些序列值之间具有密切的相关关...
import numpy as npfrom pandas 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 = seasona...
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'], ...