python实现时间序列分解法(Time-series Decomposition)——以预测气温为例 目录 程序简介 程序/数据集下载 代码分析 程序简介 利用乘法型的时间序列分解算法预测北京气温变化 程序输入:观测数据,周期长度,需要往后预测的个数 程序输出:预测值,模型结构 时间序列分解使用加法模型或乘法模型讲原始系列拆分为四部分:长期趋势...
decomposition = seasonal_decompose(timeseries,period=p) trend = decomposition.trend seasonal = decomposition.seasonal residual = decomposition.resid plt.figure(facecolor='white',figsize=(14,12)) plt.subplot(411) plt.plot(timeseries, label='Original({})'.format(timeseries.name)) plt.legend(loc='...
Visualization with seasonal trends # Decompose the time series decomposition = seasonal_decompose(data['mean_temp'], model='additive', period=12) decomposition.plot() plt.tight_layout() plt.show() Model fit # Define and fit the SARIMA model model_sarima = SARIMAX(data['mean_temp'], order=...
decomposition = seasonal_decompose(train_data, period=period) seasonal = decomposition.seasonal resid = decomposition.resid # Calculate the residuals for the test data test_decomposition = seasonal_decompose(test_data, period=period) test_residuals = test_decomposition.resid # Calculate the anomalies usi...
fromsklearnimportlinear_modelimportnumpy as npimportpandas as pddeftime_series_decomposition(data, n):"""时间序列分解 预测n个值"""#求四项平均和居中平均TC#data['四项平均'].iloc[2:len(data)-2] = data['销售额'].rolling(4).mean()[3:].tolist()data.loc[2:len(data)-2,'四项平均'] ...
decomposition=seasonal_decompose(timeseries)#timeseries时间序列数据trend=decomposition.trend seasonal=decomposition.seasonal residual=decomposition.resid 但到底是如何分解成这三部分呢,还是值得探究一下。以下写写数据的具体分解过程。 数据分解原理 decompose数据分解模型主要有两类:相加模型(additive)和相乘模型(multiplic...
def decompose(timeseries): # 返回包含三个部分 trend(趋势部分) , seasonal(季节性部分) 和residual (残留部分) decomposition = seasonal_decompose(timeseries) trend = decomposition.trend seasonal = decomposition.seasonal residual = decomposition.resid ...
df = pd.read_csv("time-series-data.csv") decomposition = STL(df['x'], period=12).fit() decomposition可以进一步绘制如下: fig, (ax1, ax2, ax3, ax4) = plt.subplots(nrows=4, ncols=1, sharex=True,figsize=(10,8)) ax1.plot(decomposition.observed) ...
11:新变量带入公式进行预测 from sklearn import linear_model居中平均 import numpy as np import pandas as pd def time_series_decomposition(data, n): """ 时间序列分解 预测n个值 """ data = pd.read_excel(u'C:\Users\liangfeng\Desktop\销售额季度数据.xlsx', sheet_name='销售额季度数据')#she...
plt.title('Time Series Plot')plt.show()2. 趋势与季节性分解 使用分解方法，如季节性调整和趋势分析，可以更好地理解数据中的模式：from statsmodels.tsa.seasonal import seasonal_decompose decomposition = seasonal_decompose(data, model='additive')trend = decomposition.trend seasonal = decomposition.seasonal ...