sma = np.r_[1, np.zeros(s-1), smaparams] # 季节性多项式系数 data = sm.tsa.arma_generate_sample(ar, ma, n, distrvs=np.random.randn, scale=0.1) seasonal_data = sm.tsa.arma_generate_sample(sar, sma, n, distrvs=np.random.randn, scale=0.1) data = data + seasonal_data print(d...
时间序列:ARMA 关于时间序列的模型有很多,我们选择ARMA模型示例,首先导入相关包并生成数据 %matplotlib inline import numpy as np import statsmodels.api as sm import pandas as pd from statsmodels.tsa.arima_process import arma_generate_sample np.random.seed(12345) arparams = np.array([.75, -.25]) ...
arma22 = smt.arma_generate_sample(ar=ar, ma=ma, nsample=n, burnin=burn) _ = tsplot(arma22, lags=max_lag) mdl = smt.ARMA(arma22, order=(2, 2)).fit( maxlag=max_lag, method='mle', trend='nc', burnin=burn) p(mdl.summary()) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11...
#这里使用arma模型进行模拟,设定ar阶数为0,即得到ma模型 alphas = np.array([0.]) betas = np.array([0.6]) ar = np.r_[1, -alphas] ma = np.r_[1, betas] #模拟MA的样本数据 ma_sample = smt.arma_generate_sample(ar=ar, ma=ma, nsample=1000) ts_plot(ma_sample, lags=30,title='MA...
4. 自回归移动平均模型(ARMA) ARMA模型结合了AR和MA模型的特点,使用时间序列的过去值和误差项的过去值作为未来值的预测因子。 复制 importnumpyasnpimportpandasaspdimportmatplotlib.pyplotasplt from statsmodels.tsa.arima_processimportarma_generate_sample
时间序列:ARMA 关于时间序列的模型有很多,我们选择ARMA模型示例,首先导入相关包并生成数据 %matplotlib inline import numpy as np import statsmodels.api as sm import pandas as pd from statsmodels.tsa.arima_process import arma_generate_sample np.random.seed(12345) arparams = np.array([.75, -.25]) ...
时间序列:ARMA 关于时间序列的模型有很多,我们选择ARMA模型示例,首先导入相关包并生成数据 代码语言:javascript 代码运行次数:0 运行 AI代码解释 %matplotlib inline import numpy as np import statsmodels.api as sm import pandas as pd from statsmodels.tsa.arima_process import arma_generate_sample np.random.se...
MA2_process=ArmaProcess(ar2, ma2).generate_sample(nsample=1000) 1. 现在,让我们可视化该过程及其相关图: 复制 plt.figure(figsize=[10, 7.5]); # Set dimensions for figureplt.plot(MA2_process)plt.title('Moving Average Process of Order 2')plt.show()plot_acf(MA2_process,lags=20); ...
ts_data = pd.Series(arma_process.generate_sample(nsample=1000)) # 定义ARIMA模型 order = (1, 0, 0) # ARIMA(1, 0, 0)模型 ar_coef = pm.Uniform('ar_coef', lower=-1, upper=1) mu = pm.Uniform('mu', lower=-1, upper=1) ...
In this recipe, we will learn how to create a modelforstationary time series datawithARMA terms. Getting ready For this recipe, we need the Matplotlib `pyplot` module importedas`plt`andthe statsmodels package `api` module importedas`sm`. We also need toimportthe `generate_sample_data` routi...