假设我们的模型是一个ARIMA(1,1,1)模型,外部变量是’temperature’和’holiday’。 # 拟合ARIMAX模型model=sm.tsa.ARIMA(data['sales'],order=(1,1,1),exog=data[['temperature','holiday']])results=model.fit()print(results.summary()) 1. 2. 3. 4. 最后,我们可以使用拟合好的模型来进行预测。 #...
for p in range(6): for q in range(6): try: model = ARIMA(scaled_data, order=(p, 1, q)) result = model.fit() aic_values[(p, 1, q)] = result.aic except: continue min_aic = min(aic_values, key=aic_values.get) print("min aci:", min_aic) model = ARIMA(scaled_data, ...
resultdf["mae"] =np.double(qq.flatten())## 迭代循环建立多个模型foriiinresultdf.index: model_i= pf.ARIMAX(data=traindata,formula="EXP~CUR+CRR+D+Trade+Invest+Rate+Gov+Pro",ar=resultdf.arp[ii],ma=resultdf.mrq[ii],integ=0)try: modeli_fit= model_i.fit("MLE") bic=modeli_fit.bic...
model = MVLSTM(X_train_t.shape[2], 1, 128).cuda() opt = torch.optim.Adam(model.parameters(), lr=0.001) epoch_scheduler = torch.optim.lr_scheduler.StepLR(opt, 20, gamma=0.9) PLANT SML 解释 在这部分,我们定性地分析变量和时间重要性的意义。图分别展示了在最佳超参数下训练期间的变量和时...
opt = torch.optim.Adam(model.parameters(), lr=0.001) epoch_scheduler = torch.optim.lr_scheduler.StepLR(opt, 20, gamma=0.9) PLANT SML 解释 在这部分,我们定性地分析变量和时间重要性的意义。图分别展示了在最佳超参数下训练期间的变量和时间重要性值。多变量- Full 和 多变量- Tensor 学习到的重要性...
opt = torch.optim.Adam(model.parameters(), lr=0.001) epoch_scheduler = torch.optim.lr_scheduler.StepLR(opt, 20, gamma=0.9) PLANT SML 解释 在这部分,我们定性地分析变量和时间重要性的意义。图分别展示了在最佳超参数下训练期间的变量和时间重要性值。多变量- Full 和 多变量- Tensor 学习到的重要性...
Hey folks! My familiarity with python and Arima is rather amateur. I am hoping you all could take a look at my notebook here and let me how to fix the SVD issue with my ARIMAX model. I am trying to test to see if S&P index has an exogeno...
The arimax() function from the TSA package fits the transfer function model (but not the ARIMAX model). This is a new package and I have not yet used it, but it is nice to finally be able to fit transfer function models in R. Sometime I plan to write a function to allow automated...
近交系数： 近交系数（inbreeding coefficient）是指根据近亲交配的世代数，将基因的纯化程度用百分数来...
resultdf["mae"] =np.double(qq.flatten())## 迭代循环建立多个模型foriiinresultdf.index: model_i= pf.ARIMAX(data=traindata,formula="EXP~CUR+CRR+D+Trade+Invest+Rate+Gov+Pro",ar=resultdf.arp[ii],ma=resultdf.mrq[ii],integ=0)try: ...